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15 pages, 7070 KiB  
Article
Assessment of Fire Dynamics in the Amazon Basin Through Satellite Data
by Humberto Alves Barbosa, Catarina Oliveira Buriti and Tumuluru Venkata Lakshmi Kumar
Atmosphere 2025, 16(2), 228; https://doi.org/10.3390/atmos16020228 - 18 Feb 2025
Abstract
The Amazon region is becoming more vulnerable to wildfires occurring in the dry season, a crisis amplified by climate change, which affects biomass burning across a wide range of forest environments. In this study, we examined the impact of seasonal fire on greenhouse [...] Read more.
The Amazon region is becoming more vulnerable to wildfires occurring in the dry season, a crisis amplified by climate change, which affects biomass burning across a wide range of forest environments. In this study, we examined the impact of seasonal fire on greenhouse (GHG) emissions over the study region during the last two decades of the 21st century by integrating calibrated and validated satellite-derived products of estimations of burned biomass area, land cover, vegetation greenness, rainfall, land surface temperature (LST), carbon monoxide (CO), and nitrogen dioxide (NO2) through geospatial techniques. The results revealed a strong impact of fire activity on GHG emissions, with abrupt changes in CO and NO2 emission factors between early and middle dry season fires (July–September). Among these seven variables analyzed, we found a positive relationship between the total biomass burned area and fire-derived GHG emission factors (r2 = 0.30) due to the complex dynamics of plant moisture and associated CO and NO2 emissions generated by fire. Nevertheless, other land surface drivers showed the weakest relationships (r2~0.1) with fire-derived GHG emissions due to other factors that drive their regional distribution. Our analysis suggests the importance of continued research on the response of fire season to other land surface characteristics that represent the processes driving fire over the study region such as fuel load, composition, and structure, as well as prevailing weather conditions. These determinants drive fire-related GHG emissions and fire-related carbon cycling relationships and can, therefore, appropriately inform policy fire-abatement guidelines. Full article
(This article belongs to the Section Air Quality)
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Figure 1

Figure 1
<p>The Amazon basin. (<b>a</b>) the spatial distribution of annual burned biomass areas from 2001 to 2020 in four distinct seasons. The colors of each plot indicate: rainy season (December to June), early dry season (July), middle dry season (August–September), or late dry season (October–November). (<b>b</b>) The monthly distribution of total number of fire events identified in the years 2015 to 2020.</p>
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<p>Schematic representation of the hydrographic network superimposed over the Amazon basin. (<b>a</b>) Average annual rainfall from the Climate Hazards Group Infrared Precipitation with Stations (CHIRPS) for the baseline (2001–2020). (<b>b</b>) The land-cover map. (<b>c</b>) Topographic relief based on 250 m Digital Elevation Model—Shuttle Radar Topographic Mission (DEM-SRTM) images. (<b>d</b>) Köppen–Geiger climate classification map.</p>
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<p>(<b>a</b>) The annual forest cover change (% of grid cell area) between 2001 and 2020 calculated using the percentage change method applied to the MODIS land-cover data. (<b>b</b>) Variability of monthly normalized values of NDVI (the green curve), CO (the orange curve), NO<sub>2</sub> (the yellow curve), and burned biomass (the red curve) parameters for their available periods between 2001 and 2020 over the study region. The three parameters (CO, NO<sub>2</sub>, and burned biomass) were normalized to be between 0 and 1, using the min-max normalization method. Fire-derived CO and NO<sub>2</sub> emission parameters are usually quantified using an emission factor, a ratio indicating the proportion of each chemical species that is emitted per unit of biomass burning.</p>
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<p>Scatterplot of (<b>a</b>) six different parameters of principal components (PC2 versus PC1 loadings). Scatterplot of (<b>b</b>) fire per month of PC2 versus PC1 loadings. The percentage indicates the total variance. The arrow widths on the figures are proportional to the PC loadings (positive or negative) regressed upon a fire driver and fire per month. Color symbols indicate mean loadings of PC1 and PC2.</p>
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31 pages, 5400 KiB  
Article
Development and Validation of Data Acquisition System for Real-Time Thermal Environment Monitoring in Animal Facilities
by Carlos Eduardo Alves Oliveira, Thalya Aleixo Avelar, Ilda de Fátima Ferreira Tinôco, André Luiz de Freitas Coelho, Fernanda Campos de Sousa and Matteo Barbari
AgriEngineering 2025, 7(2), 45; https://doi.org/10.3390/agriengineering7020045 - 17 Feb 2025
Abstract
In animal facilities, monitoring and controlling the thermal environment are essential in ensuring productivity and sustainability. However, many production units face challenges in implementing and maintaining effective thermal monitoring and control systems. Given the need for Smart Livestock Farming systems, this study aimed [...] Read more.
In animal facilities, monitoring and controlling the thermal environment are essential in ensuring productivity and sustainability. However, many production units face challenges in implementing and maintaining effective thermal monitoring and control systems. Given the need for Smart Livestock Farming systems, this study aimed to develop and validate an easy-to-use, low-cost embedded system (ESLC) for the real-time monitoring of dry-bulb air temperature (Tdb, in °C) and relative humidity (RH, in %) in animal production facilities. The ESLC consists of data collection/transmission modules and a server for Internet of Things (IoT) data storage. ESLC modules and standard recording sensors (SRS) were installed in prototype animal facilities. Over 21 days, their performance was evaluated based on the Data Transmission Success Rate (DTSR, in %) and Data Transmission Interval (DTI, in minutes). Additionally, agreement between the ESLC modules and the SRS was assessed using the daily mean root mean square error (RMSE) and mean relative error (RE) across different Tdb and RH ranges. The ESLC successfully collected and transmitted data to the cloud server, achieving an average DTSR of 94.04% and a predominant DTI of one minute. Regarding measurement agreement, distinct daily mean RMSE values were obtained for Tdb (0.26–2.46 °C) and RH (4.37–16.20%). Furthermore, four sensor modules exhibited mean RE values below 3.00% across all Tdb ranges, while all sensor modules showed progressively increasing mean RE values as RH levels rose. Consequently, calibration curves were established for each sensor module, achieving a high correlation between raw and corrected values (determination coefficient above 0.98). It was concluded that the ESLC is a promising solution for thermal monitoring in animal facilities, enabling continuous and reliable data collection and transmission. Full article
(This article belongs to the Section Livestock Farming Technology)
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Graphical abstract

Graphical abstract
Full article ">Figure 1
<p>Schematic diagram of the master module’s electronic circuit of the real-time thermal environment monitoring embedded system. Note: 1—ESP32 WROOM-32D Board; 2—BME280 Sensor Module; 3—NRF24L01 Wireless Transceiver Module; 4—OLED Display Module; 5—real-time clock (RTC) module; 6—micro SD card module.</p>
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<p>Detailed schematic diagram of the hardware assembly for the “master” embedded system module (<b>a</b>) and the master module during installation in a prototype animal facility within the experimental area where it was evaluated (<b>b</b>). Note: 1—ESP32 WROOM-32D Board; 2—BME280 Sensor Module; 3—NRF24L01 Wireless Transceiver Module; 4—OLED Display Module; 5—real-time clock (RTC) module; 6—micro SD card module; 7—CCE 50 × 2-pair cable; 8—perforated phenolic board; 9—terminal adapter × P4 female plug; 10—PB-114 Case; 11—metal hook.</p>
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<p>Distribution of prototype animal facilities in the experimental area where the embedded system for real-time thermal environment monitoring was validated (<b>a</b>); schematic cross-sectional representation of an animal housing prototype, highlighting the positioning of the sensor modules (<b>b</b>). Note: N—north direction; ID—identification number of each sensor module; <span class="html-italic">i</span>—roof slope; ID = 1—master module; ID = 2—secondary module 1; ID = 3—secondary module 2; ID = 4—secondary module 3; ID = 5—secondary module 4; ID = 6—secondary module 5; measurements in metres (m).</p>
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<p>Distribution of data transmission failures from the embedded thermal environment monitoring system to the IoT cloud server over 24 h for each experimental day. Each red dot represents a transmission failure; red dots in close proximity should not necessarily be interpreted as cumulative transmission delays, as the graph’s scale does not allow for detailed observation of the intervals between closely occurring failures.</p>
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<p>Mean relative error (RE, in %) curves by range for the variables monitored via embedded system: dry-bulb air temperature—T<sub>db</sub>, in °C (<b>a</b>), and relative humidity—RH, in % (<b>b</b>).</p>
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<p>Calibration curves for dry-bulb air temperature (T<sub>db</sub>, in °C), obtained for each sensor module that forms the developed embedded system: modules BME<sub>1</sub> (<b>a</b>), BME<sub>2</sub> (<b>b</b>), BME<sub>3</sub> (<b>c</b>), BME<sub>4</sub> (<b>d</b>), BME<sub>5</sub> (<b>e</b>), and BME<sub>6</sub> (<b>f</b>). T<sub>db-Adj</sub>—corrected dry-bulb air temperature, in °C; T<sub>db-R</sub>—dry-bulb air temperature read by the BME280 sensor modules, in °C; R<sup>2</sup>—coefficient of determination.</p>
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<p>Calibration curves for relative humidity (RH, in %) obtained for each sensor module in the developed embedded system: modules BME<sub>1</sub> (<b>a</b>), BME<sub>2</sub> (<b>b</b>), BME<sub>3</sub> (<b>c</b>), BME<sub>4</sub> (<b>d</b>), BME<sub>5</sub> (<b>e</b>), and BME<sub>6</sub> (<b>f</b>). RH<sub>Adj</sub>—corrected relative humidity, in %; RH<sub>R</sub>—relative humidity readings by the BME280 sensor modules, in %; R<sup>2</sup>—coefficient of determination.</p>
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<p>Time–series curves obtained using corrected data from the variables monitored via the embedded system: dry-bulb air temperature—T<sub>db</sub>, in °C (<b>a</b>); relative humidity—RH, in % (<b>b</b>).</p>
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30 pages, 16755 KiB  
Article
Liquid Crystal Thermography and Infrared Thermography Application in Heat Transfer Research on Flow Boiling in Minichannels
by Magdalena Piasecka, Artur Piasecki and Beata Maciejewska
Energies 2025, 18(4), 940; https://doi.org/10.3390/en18040940 - 16 Feb 2025
Abstract
This study investigated FC-72 boiling heat transfer in minichannels using two non-contact temperature measurement techniques: liquid crystal thermography (LCT) and infrared thermography (IRT). These methods were applied simultaneously to measure temperature distributions on the heated wall surface of minichannels, formed by a thin [...] Read more.
This study investigated FC-72 boiling heat transfer in minichannels using two non-contact temperature measurement techniques: liquid crystal thermography (LCT) and infrared thermography (IRT). These methods were applied simultaneously to measure temperature distributions on the heated wall surface of minichannels, formed by a thin metal foil. The temperature data facilitated the calculation of local heat transfer coefficients at the foil–working fluid contact surface. Calibration of the liquid crystal colour response to temperature was conducted prior to the use of LCT. According to a comparison of the heat transfer coefficients and Nusselt numbers determined using LCT and IRT measurements, comparable temperature distributions are provided, with the average relative differences in heat transfer coefficients determined using these techniques remaining below 15%. The findings highlight the advantages of non-contact temperature measurement in minimising system disturbances while providing precise data for understanding flow boiling heat transfer mechanisms. Such results can contribute to the design of minichannel heat exchangers. Full article
(This article belongs to the Section J1: Heat and Mass Transfer)
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Figure 1

Figure 1
<p>Diagrams of (<b>a</b>,<b>b</b>) the experimental stand; (<b>c</b>,<b>d</b>) the test section, in which the temperature measurement of the heated foil was provided by LCT—LCT minichannel (<b>c</b>) and IRT—IRT minichannel; and (<b>d</b>) 1—test section, 2, 22—circulating pumps, 3—compensating tank, 4—heat exchanger, 5, 18, 21—filters, 6—flow meter, 7, 20—deaerators, 8—pressure meter, 9—a data acquisition station, 10—computer, 11—infrared camera, 12—digital camera, 13—lighting system, 14—power source, 15—shunt, 16—ammeter, 17—voltmeter, 19—electric heater, 23—autotransformer, 24—minichannel, 25—heated foil, 26—liquid crystal layer, 27—glass pane, 28—body, 29—front cover, 30—thermocouple, 31—black paint layer.</p>
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<p>Schematic of the flow system for the liquid crystal calibration experiment: 1—test section with minichannels (an LCT channel was considered), 2—circulation pump, 3—tank with electric heater, T—K-type thermocouple, <span class="html-italic">p</span>—pressure meter.</p>
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<p>Thermographic LCT images, collected when the LCR Hallcrest R40C20W liquid crystal mixture was used in the calibration experiment.</p>
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<p>The calibration curve: <math display="inline"><semantics> <mrow> <msub> <mi>T</mi> <mrow> <mi>C</mi> <mi>C</mi> </mrow> </msub> <mfenced> <mrow> <mi>h</mi> <mi>u</mi> <mi>e</mi> </mrow> </mfenced> <mo>=</mo> <mo>−</mo> <mn>676.61</mn> <mo>+</mo> <mn>70.13</mn> <mi>h</mi> <mi>u</mi> <mi>e</mi> <mo>−</mo> <mn>2.84</mn> <mi>h</mi> <mi>u</mi> <msup> <mi>e</mi> <mn>2</mn> </msup> <mo>+</mo> <mn>0.06</mn> <mi>h</mi> <mi>u</mi> <msup> <mi>e</mi> <mn>3</mn> </msup> <mo>−</mo> <mn>0.00083</mn> <mi>h</mi> <mi>u</mi> <msup> <mi>e</mi> <mn>4</mn> </msup> <mo>+</mo> <mn>0.0000066</mn> <mi>h</mi> <mi>u</mi> <msup> <mi>e</mi> <mn>5</mn> </msup> <mo>−</mo> <mn>3.18</mn> <mo>×</mo> <msup> <mrow> <mn>10</mn> </mrow> <mrow> <mo>−</mo> <mn>8</mn> </mrow> </msup> <mi>h</mi> <mi>u</mi> <msup> <mi>e</mi> <mn>6</mn> </msup> <mo>+</mo> <mn>8.35</mn> <mo>×</mo> <msup> <mrow> <mn>10</mn> </mrow> <mrow> <mo>−</mo> <mn>11</mn> </mrow> </msup> <mi>h</mi> <mi>u</mi> <msup> <mi>e</mi> <mn>7</mn> </msup> <mo>−</mo> <mn>9.24</mn> <mo>×</mo> <msup> <mrow> <mn>10</mn> </mrow> <mrow> <mo>−</mo> <mn>14</mn> </mrow> </msup> <mi>h</mi> <mi>u</mi> <msup> <mi>e</mi> <mn>8</mn> </msup> </mrow> </semantics></math>.</p>
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<p>View of the test section, in front of which three infrared cameras were positioned.</p>
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<p>Example thermograms of the foil surface (central fragment), recorded using FLIR thermal imaging cameras: E96 (<b>a</b>), A655sc (<b>b</b>), and E60 (<b>c</b>).</p>
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<p>Example thermogram of the foil surface, with indicated position of the selected fragment centrally located on the entire foil surface.</p>
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<p>Temperature distributions, obtained from thermograms recorded using FLIR thermal cameras, models: E96 (<b>a</b>), A655sc (<b>b</b>), and E60 (<b>c</b>), based on the data shown in <a href="#energies-18-00940-f002" class="html-fig">Figure 2</a>.</p>
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<p>(<b>a</b>) Box-and-whisker plots for the measurements of heated foil surface temperature and (<b>b</b>) subsequent foil temperature measurements; recorded using FLIR thermal cameras, models: E96, A655sc, and E60.</p>
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<p>Thermograms on the outer heated foil surface, recorded using: (<b>a</b>) liquid crystal thermography and (<b>b</b>) infrared thermography. Experimental parameters: mass flux 139 kg/(m<sup>2</sup> s), average pressure at the inlet of the minichannels: 171 kPa, heat flux during the increase (settings from #1 to #8, i.e., from 10.8 kW/m<sup>2</sup> to 11.8 kW/m<sup>2</sup>) and its subsequent decrease (settings from #9 to #16, i.e., from 10.7 kW/m<sup>2</sup> to 5.1 kW/m<sup>2</sup>).</p>
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<p>Foil temperature as a function of distance from minichannel inlet; results from LCT thermograms, (<b>a</b>) settings from #1 to #8, (<b>b</b>) settings from #9 to #16. Experimental parameters are same as for <a href="#energies-18-00940-f010" class="html-fig">Figure 10</a>.</p>
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<p>Foil temperature as a function of distance from minichannel inlet; results of IRT thermograms, (<b>a</b>) settings from #1 to #8, (<b>b</b>) settings from #9 to #16. Experimental parameters are same as for <a href="#energies-18-00940-f010" class="html-fig">Figure 10</a>.</p>
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<p>Dependence of the heat transfer coefficient as a function of distance from the minichannel inlet; results of the LCT thermograms, (<b>a</b>) settings from #1 to #8, (<b>b</b>) settings from #9 to #16. Experimental parameters are the same as for <a href="#energies-18-00940-f010" class="html-fig">Figure 10</a>.</p>
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<p>Dependence of heat transfer coefficient as a function of distance from the minichannel inlet; results from the IRT thermograms, (<b>a</b>) settings from #1 to #8, (<b>b</b>) settings from #9 to #16. Experimental parameters are the same as for <a href="#energies-18-00940-f010" class="html-fig">Figure 10</a>.</p>
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<p>Dependence of the Nusselt number as a function of distance from the minichannel inlet; results from LCT thermograms, (<b>a</b>) settings from #1 to #8, (<b>b</b>) settings from #9 to #16. Experimental parameters are the same as for <a href="#energies-18-00940-f010" class="html-fig">Figure 10</a>.</p>
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<p>Dependence of the Nusselt number as a function of distance from the minichannel inlet; results from IRT thermograms, (<b>a</b>) settings from #1 to #8, (<b>b</b>) settings from #9 to #16. Experimental parameters are the same as for <a href="#energies-18-00940-f010" class="html-fig">Figure 10</a>.</p>
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<p>Boiling curve, constructed for the distance 0.3 m from the inlet. Experimental parameters the same as for <a href="#energies-18-00940-f010" class="html-fig">Figure 10</a>, based on IRT temperature data.</p>
Full article ">Figure 18
<p>Typical boiling curve presenting nucleation hysteresis, constructed for the distance 0.2 m from the inlet, experimental parameters: FC-72, horizontal minichannel with fluid flow above the heated wall, heated foil with enhanced surface (surface with mini-recesses), mass flux of 285 kg/(m<sup>2</sup> s), pressure at the inlet of 120 kPa, inlet liquid subcooling of 44 K, ONB—onset of boiling; based on LCT temperature data.</p>
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<p>(<b>a</b>) LCT images from the experimental series, which provide temperature-based data to construct the boiling curve illustrated in <a href="#energies-18-00940-f018" class="html-fig">Figure 18</a> and (<b>b</b>) two-phase flow pattern images corresponding to selected LCT images; ONB—onset of boiling. Cross-section marked with a dashed line refers to the boiling curve illustrated in <a href="#energies-18-00940-f018" class="html-fig">Figure 18</a>.</p>
Full article ">Figure 20
<p>The boiling curve indicated the stepped course of nucleation hysteresis, constructed for the distance 0.1 m from the inlet, experimental parameters: FC-72, vertical minichannel with upward fluid flow, heated foil with enhanced surface (surface with mini-recesses), mass flux of 286 kg/(m<sup>2</sup> s), pressure at the inlet of 122 kPa, inlet liquid subcooling of 43 K, ONB—onset of boiling, based on LCT temperature data.</p>
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<p>Comparison of the heat transfer coefficient calculated on the basis of temperature data from LCT measurements (named ‘experimental’) with values determined from selected correlations from the literature, i.e., [<a href="#B27-energies-18-00940" class="html-bibr">27</a>,<a href="#B28-energies-18-00940" class="html-bibr">28</a>,<a href="#B29-energies-18-00940" class="html-bibr">29</a>,<a href="#B30-energies-18-00940" class="html-bibr">30</a>,<a href="#B31-energies-18-00940" class="html-bibr">31</a>] (named ‘theoretical’); experimental parameters the same as for <a href="#energies-18-00940-f010" class="html-fig">Figure 10</a>; heat flux settings: #4 to #12; distance of 0.2 m from the inlet (marked with a dashed line in <a href="#energies-18-00940-f019" class="html-fig">Figure 19</a>).</p>
Full article ">
24 pages, 6680 KiB  
Article
Bioclimatic Design Guidelines for Design Decision Support to Enhance Residential Building Thermal Performance in Tropical Regions
by Kimnenh Taing, Sigrid Reiter, Virak Han and Pierre Leclercq
Sustainability 2025, 17(4), 1591; https://doi.org/10.3390/su17041591 - 14 Feb 2025
Abstract
With the rise of building thermal comfort issues, the Bioclimatic Design Guideline for Cambodia (BDGC) has been developed to help architects make informed decisions during their design process to achieve maximum thermal comfort with minimum energy consumption. This paper aims to investigate the [...] Read more.
With the rise of building thermal comfort issues, the Bioclimatic Design Guideline for Cambodia (BDGC) has been developed to help architects make informed decisions during their design process to achieve maximum thermal comfort with minimum energy consumption. This paper aims to investigate the reliability of this guideline as decision support to enhance residential building thermal performance by using two research approaches: usability tests and calibrated thermal performance simulations based on real buildings monitoring and simulations using DesignBuilder. Five groups of architects and students in architectural engineering participated in the usability test to redesign two common typologies of single-family homes with weak thermal performance by using bioclimatic design guidelines, such as orientation, improved ventilation, shading, and green rood, to enhance their comfort level. The simulation shows that, by applying bioclimatic design strategies, the indoor temperature in the base case house can be lower from 2 to 4 °C. Various benefits are identified from the integration of the BDGC during the design process for improving residential building design. Moreover, the proposed methodology can be applied to develop and validate bioclimatic guidelines in other regions and various countries worldwide. Full article
(This article belongs to the Section Green Building)
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Figure 1

Figure 1
<p>The development of the BDGC.</p>
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<p>Bioclimatic design guidelines for Cambodia in the form of cards.</p>
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<p>Process of the usability test.</p>
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<p>Plan and view the selected base case for the link house.</p>
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<p>Plan and view of the selected base case for the detached house.</p>
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<p>Link house bioclimatic scenario 1.</p>
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<p>Link house bioclimatic scenario 2.</p>
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<p>Detached house bioclimatic scenario 1.</p>
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<p>Detached house bioclimatic scenario 2.</p>
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<p>Design process practiced during the usability test.</p>
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<p>Air temperature of the bioclimatic house compared to the base case for a link house.</p>
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<p>Air temperature of the bioclimatic houses compared to the base case for the detached house.</p>
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19 pages, 5449 KiB  
Article
Space-Based Limb-Imaging Spectrometer for Atmospheric O2 Airglow Detection
by Minjie Zhao, Haijin Zhou, Yu Jiang, Shuhua Huang, Xin Zhao, Yi Zeng, Jun Chen, Fenglei Liu, Xiaohan Qiu, Quan Zhang, Lei Zhu, Shimei Wang, Kai Zhan, Ge Yan and Fuqi Si
Atmosphere 2025, 16(2), 214; https://doi.org/10.3390/atmos16020214 - 13 Feb 2025
Abstract
This paper presents a space-based limb-imaging spectrometer (LIS) for detecting atmospheric O2 airglow; it scans the atmosphere with a vertical range of 10–100 km and has a vertical resolution of 2 km. The LIS’s detection performance needs to be examined before launch. [...] Read more.
This paper presents a space-based limb-imaging spectrometer (LIS) for detecting atmospheric O2 airglow; it scans the atmosphere with a vertical range of 10–100 km and has a vertical resolution of 2 km. The LIS’s detection performance needs to be examined before launch. A forward radiative transfer model (RTM) of airglow is studied to determine the airglow emission intensity. Spectral and radiation calibration is conducted to obtain the response parameters. Based on the airglow emission intensity, calibration results, and airglow spectral lines, the LIS’s simulated spectra are obtained, and then an optimal estimation inversion method for the LIS is studied. The results show that the LIS’s spectral range is 498.1 nm–802.3 nm, with a spectral resolution of 1.38 nm. Simulation results show that the LIS can detect airglow emission spectral lines, which characterize their dependence on temperature. The digital number response value is 20% to 50% of the saturation value. An inversion error analysis shows that, when the signal-to-noise ratio (SNR) of the LIS is 1000 and the prior temperature error is 10%, the inversion errors are 6.2 and 3 K at 63 and 77 km, respectively. This study shows that the LIS can achieve good SNR detection for airglow. Full article
(This article belongs to the Section Atmospheric Techniques, Instruments, and Modeling)
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Figure 1

Figure 1
<p>Illustration of the airglow limb-viewing line of sight.</p>
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<p>Limb-viewing geometry of the LIS, solid and dashed lines represent the start and end of the scanning, respectively.</p>
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<p>Components of the LIS system: grating spectrometer, scanning mirror, scrambler, and calibration module.</p>
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<p>The LIS’s optical system. Left: pre-optical module. Right: spectroscopic module.</p>
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<p>Data chain of the LIS.</p>
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<p>Calibration system of the LIS, blue lines represent pen shaped lamps inculing Hg-Ar and Ne lamp.</p>
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<p>Calibration data processing chart.</p>
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<p>Airglow observed by SCIAMACHY for the A band at 760 nm (<b>a</b>) and the IR band at 1270 nm (<b>b</b>).</p>
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<p>Transmittance in the transmitting segment for the A band at 760 nm (<b>a</b>) and the IR band at 1270 nm (<b>b</b>).</p>
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<p>Wavelength-integrated spectral observed radiance (black), transmitting segment radiance (blue), and emitting segment radiance (red) for the A band at 760 nm (<b>a</b>) and the IR band at 1270 nm (<b>b</b>). The observed radiance is the sum of the transmitting segment radiance and the emitting segment radiance.</p>
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<p>The VERs retrieved from SCIAMACHY using the onion-peeling method for the A band at 760 nm (<b>a</b>) and the IR band at 1270 nm (<b>b</b>).</p>
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<p>Airglow spectral lines calculated using the forward model for the A band at 760 nm (<b>a</b>) and the IR band at 1270 nm (<b>b</b>).</p>
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<p>Spectral calibration results with an entrance slit width of 45 μm: (<b>a</b>) spectral imaging of a full field of view; (<b>b</b>) spectral line in the center field of view; (<b>c</b>) Gaussian fitting of the spectral line.</p>
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<p>Radiometric calibration results: (<b>a</b>) input radiance and output DN; (<b>b</b>) response coefficient.</p>
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<p>Response simulation of the LIS for the A band at 760 nm airglow with an FWHM of 1.38 nm.</p>
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<p>The LIS’s SNR with the binning of 200 rows.</p>
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<p>Weighting function (<b>a</b>) and averaging kernel (<b>b</b>) of LIS response simulation airglow spectra.</p>
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<p>Retrieval error, smoothing error, and total error for temperature inversion from the LIS response simulation airglow spectra: errors in the case of a temperature prior of 20% and an SNR value of 500 (<b>a</b>), and errors in the case of a temperature prior of 10% and an SNR value of 1000 (<b>b</b>).</p>
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<p>Temperature profile inverted from SCAIMACHY spectra using the OE method.</p>
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33 pages, 5254 KiB  
Article
Effective Thermal Diffusivity Measurement Using Through-Transmission Pulsed Thermography: Extending the Current Practice by Incorporating Multi-Parameter Optimisation
by Zain Ali, Sri Addepalli and Yifan Zhao
Sensors 2025, 25(4), 1139; https://doi.org/10.3390/s25041139 - 13 Feb 2025
Abstract
Through-transmission pulsed thermography (PT) is an effective non-destructive testing (NDT) technique for assessing material thermal diffusivity. However, the current literature indicates that the technique has lagged behind the reflection mode in terms of technique development despite it offering better defect resolution and the [...] Read more.
Through-transmission pulsed thermography (PT) is an effective non-destructive testing (NDT) technique for assessing material thermal diffusivity. However, the current literature indicates that the technique has lagged behind the reflection mode in terms of technique development despite it offering better defect resolution and the detection of deeper subsurface defects. Existing thermal diffusivity measurement systems require costly setups, including temperature-controlled chambers, multiple calibrations, and strict sample size requirements. This study presents a simple and repeatable methodology for determining thermal diffusivity in a laboratory setting using the through-transmission approach by incorporating both finite element analysis (FEA) and laboratory experiments. A full-factorial design of experiments (DOE) was implemented to determine the optimum flash energy and sample thickness for a reliable estimation of thermal diffusivity. The thermal diffusivity is estimated using the already established Parker’s half-rise equation and the recently developed new least squares fitting (NLSF) algorithm. The latter not only estimates thermal diffusivity but also provides estimates for the input flash energy, reflection coefficient, and the time delay in data capture following the flash event. The results show that the NLSF is less susceptible to noise and offers more repeatable values for thermal diffusivity measurements compared to Parker, thereby establishing it as a more efficient and reliable technique. Full article
(This article belongs to the Section Physical Sensors)
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<p>(<b>a</b>) Illustration of the PT transmission mode configuration; (<b>b</b>) nondimensionalised back wall temperature plot displaying the value for ω at half the maximum temperature.</p>
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<p>Normalised back wall temperature profile at different emissivity values.</p>
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<p>Geometry for the FEA Model. (<b>a</b>) The yellow region is where the temperature measurements are taken from at the back wall. (<b>b</b>) The meshed geometry is where the region of interest has a finer geometry to save computational time.</p>
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<p>Thermal diffusivity measurements using the NLSF algorithm and Parker’s method for a 150 × 100 mm S275 steel plate with multiple thicknesses, subjected to different energy levels of pulsed heating simulated in COMSOL. Thermal diffusivity estimates at (<b>a</b>) 2.4kJ (<b>b</b>) 4.8kJ and (<b>c</b>) 9.6kJ.</p>
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<p>Measurement error for NLSF and Parker’s method across all thicknesses (values have been averaged across the three different energy levels).</p>
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<p>Error analysis of the NLSF algorithm with varying SNRS for (<b>a</b>) thermal diffusivity measurement and (<b>b</b>) input energy measurement using the temperature plots obtained from the FEA model in COMSOL.</p>
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<p>Comparison of Parker’s and NLSF method’s performance at estimating thermal diffusivity at different SNRs for energy levels (<b>a</b>) 2.4 kJ, (<b>b</b>) 4.8 kJ, and (<b>c</b>) 9.6 kJ.</p>
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<p>Comparison of Parker’s and NLSF method’s performance at estimating thermal diffusivity at different SNRs for energy levels (<b>a</b>) 2.4 kJ, (<b>b</b>) 4.8 kJ, and (<b>c</b>) 9.6 kJ.</p>
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<p>Pareto charts for standardised effects for the finite element simulations (<b>a</b>) thermal diffusivity estimated using NLSF and (<b>b</b>) thermal diffusivity estimated using Parker’s method.</p>
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<p>Residual plots from the finite element simulations for estimating thermal diffusivity using (<b>a</b>) NLSF and (<b>b</b>) Parker’s method.</p>
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<p>Thermal diffusivity estimations for different thicknesses at (<b>a</b>) 2.4kJ, (<b>b</b>) 4.8kJ, and (<b>c</b>) 9.6kJ using NLSF and Parker’s methods.</p>
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<p>Thermal diffusivity estimations for different thicknesses at (<b>a</b>) 2.4kJ, (<b>b</b>) 4.8kJ, and (<b>c</b>) 9.6kJ using NLSF and Parker’s methods.</p>
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<p>Thermal diffusivity measurements for varying thicknesses at different energy levels, estimated using (<b>a</b>) NLSF and (<b>b</b>) Parker’s method.</p>
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<p>Histograms showcasing variations in thermal diffusivity with different thicknesses at different energy levels estimated using NLSF (<b>a</b>,<b>c</b>,<b>e</b>) and Parker’s methods (<b>b</b>,<b>d</b>,<b>f</b>).</p>
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<p>Pareto charts for standardised effects for the laboratory experiments (<b>a</b>) thermal diffusivity estimated using NLSF and (<b>b</b>) thermal diffusivity estimated using Parker’s method.</p>
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<p>Comparison of the coefficient of variation (<b>a</b>) comparison between the NLSF and Parker methods, (<b>b</b>) variation at different thicknesses using the NLSF method, and (<b>c</b>) variation at different thicknesses using Parker’s method.</p>
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<p>Comparison of the coefficient of variation (<b>a</b>) comparison between the NLSF and Parker methods, (<b>b</b>) variation at different thicknesses using the NLSF method, and (<b>c</b>) variation at different thicknesses using Parker’s method.</p>
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<p>Comparison between simulation and laboratory experiments for thermal diffusivity measurement (<b>a</b>) using the NLSF method and (<b>b</b>) using Parker’s method.</p>
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<p>Residual plots from laboratory experiments for estimating thermal diffusivity using (<b>a</b>) NLSF and (<b>b</b>) Parker.</p>
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24 pages, 3511 KiB  
Review
A Review on the Behavior of Ultra-High-Performance Concrete (UHPC) Under Long-Term Loads
by Nermin Redžić, Nikola Grgić and Goran Baloević
Buildings 2025, 15(4), 571; https://doi.org/10.3390/buildings15040571 - 13 Feb 2025
Abstract
This paper provides a research review regarding the creep of ultra-high-performance concrete with or without the addition of fibers. Unlike other similar studies that mainly considered influential factors and their effects on the creep behavior, this research focuses more attention on the analysis [...] Read more.
This paper provides a research review regarding the creep of ultra-high-performance concrete with or without the addition of fibers. Unlike other similar studies that mainly considered influential factors and their effects on the creep behavior, this research focuses more attention on the analysis of UHPC creep models. For the creep strain assessments of these concretes, the creep models given in the latest standards cannot be used, but it is necessary to modify them to give reliable results, given the rather complex composition of UHPC. Several proposed creep models for UHPC are presented with comparative analysis. The observation is that by varying key parameters such as compressive strength, relative humidity, cross-sectional dimensions, and temperature, there may be major discrepancies between models, so additional experimental investigations are necessary to perform their calibration. In this paper, the parameters α1, α2, and γ of FIB Model Code 2010 have been modified in order to obtain a match with other proposed models in terms of the final value of the creep coefficient and the creep curve. The creep coefficient of the UHPC decreases when steel fiber content increases, but it is important to consider the excessive fiber addition because very often it causes an increase in creep strain. The application of thermal treatment at a temperature of 90 °C for 48 h significantly improves the time-dependent properties of UHPC. An analysis of the impact of the steel fiber content, fiber type, thermal treatment, and the age of the concrete under load on strains of UHPC specimens and beams under long-term loads is performed. Full article
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<p>UHPC creep coefficients [<a href="#B18-buildings-15-00571" class="html-bibr">18</a>].</p>
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<p>Results comparison of the modified model with experimental data: (<b>a</b>) C16SF1 and (<b>b</b>) C22SF2.</p>
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<p>Comparison of experimental results with results obtained according to standard: (<b>a</b>) original FIB MC 2010, and (<b>b</b>) modified FIB MC 2010 (B—basic, D—drying).</p>
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<p>Demonstration of the UHPC creep development according to the test results and proposed simplified formula [<a href="#B25-buildings-15-00571" class="html-bibr">25</a>].</p>
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<p>Diagrams <span class="html-italic">φ</span>–<span class="html-italic">t</span> for all UHPC creep models.</p>
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<p>Creep coefficients of the compressed zone of the tested beams.</p>
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<p>Load–deflection curves for all eight tested hybrid beams [<a href="#B62-buildings-15-00571" class="html-bibr">62</a>].</p>
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<p>Cross-sections of the hybrid specimens.</p>
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<p>Effect of a fraction of depth (<span class="html-italic">h</span>) of steel fibers on the failure load [<a href="#B66-buildings-15-00571" class="html-bibr">66</a>].</p>
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<p>Specific creep of normal concrete (F0) and concrete with the addition of different fibers [<a href="#B73-buildings-15-00571" class="html-bibr">73</a>].</p>
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<p>Specific creep curves of UHPC specimens reinforced with different types of fibers [<a href="#B36-buildings-15-00571" class="html-bibr">36</a>].</p>
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17 pages, 12255 KiB  
Article
Thermochromically Enhanced Lubricant System for Temperature Measurement in Cold Forming
by Christoph Kuhn, Patrick Volke and Peter Groche
Processes 2025, 13(2), 513; https://doi.org/10.3390/pr13020513 - 12 Feb 2025
Abstract
Cold forming offers high dimensional accuracy, energy and cost efficiency in the mass production of highly stressed components but is also associated with high tribological loads. Complex lubrication systems are required to ensure smooth production. As environmental standards rise, traditional zinc phosphate-based lubricants [...] Read more.
Cold forming offers high dimensional accuracy, energy and cost efficiency in the mass production of highly stressed components but is also associated with high tribological loads. Complex lubrication systems are required to ensure smooth production. As environmental standards rise, traditional zinc phosphate-based lubricants are to be replaced by less harmful single-layer systems. However, these new lubricants are temperature-sensitive, which requires precise knowledge of the temperatures in the forming zone for optimal design. Due to high compressive stress, conventional measuring methods cannot measure temperatures directly in the forming zone. In this work, lubricants are expanded into a temperature sensor using thermochromic pigments so that temperatures can be measured directly in the forming zone. This work outlines the selection and integration of the indicators, the development of a calibration method for thermochromic lubricants to characterize the correlation between colour value and temperature. It is shown that the lubricant behaviour does not deteriorate up to concentrations of 10%. The transfer of the measurement methodology from the laboratory application to the industrial multi-stage process has been successfully implemented and local temperature peaks are measured directly in the contact zone and correspond to the simulation results. The results of the work show an approach to closing the gap identified in existing research work, namely that the temperature cannot be measured directly in the forming zone during cold forging. The measuring system developed can be transferred to various processes in the future and contribute to the identification of correlations between temperature, lubricant failure and wear. Full article
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<p>Calibration principle: Step 1: Heating up with the schematic representation of the setup (<b>a</b>), the temperature curves associated with inductors (<b>b</b>) and the thermographic image of the sample (<b>c</b>). Step 2: Recording the colour values in an integrating sphere (<b>d</b>), colour image (<b>e</b>) with extracted section (<b>f</b>) Step 3: Superimposition of temperature profile and colour (<b>g</b>).</p>
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<p>Evaluation ranges for indicator 1 (<b>a</b>), indicator 2 (<b>b</b>) and indicator 3 (<b>c</b>), and evaluation applied to an example component (<b>d</b>).</p>
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<p>Functional principle sliding compression test.</p>
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<p>Influence of the indicator concentration on the CoF (three replicates).</p>
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<p>Coefficients of friction as a dependence of the contact pressure (three replicates).</p>
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<p>Detected temperature of the samples after sliding (Contact pressure 1500 kN, sliding velocity 150 mm/s).</p>
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<p>Comparison of simulated and experimentally determined force (<b>a</b>) and temperature (<b>b</b>).</p>
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<p>Stage sequence of the multi-stage process.</p>
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<p>Structure of the rotating unit (<b>b</b>) and unwound lateral surface (<b>a</b>).</p>
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<p>FEM simulation results for the temperature and the normal contact pressure of the successive stages.</p>
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<p>Position of the measuring points on the sample (<b>a</b>), roughness as a function of the indicators and measuring point before and after forming (<b>b</b>) and exemplary development of the surface topography (<b>c</b>).</p>
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<p>Original image (<b>left</b>), coloured image for temperatures above 260 °C (<b>middle</b>) and process stages (<b>right</b>).</p>
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31 pages, 8073 KiB  
Article
Optimising Ventilation Strategies for Improved Driving Range and Comfort in Electric Vehicles
by Matisse Lesage, David Chalet and Jérôme Migaud
World Electr. Veh. J. 2025, 16(2), 98; https://doi.org/10.3390/wevj16020098 - 12 Feb 2025
Abstract
A car cabin’s small volume makes it vulnerable to discomfort if temperature, humidity, and carbon dioxide levels are poorly regulated. In electric vehicles, the HVAC system draws energy from the car battery, reducing the driving range by several dozen kilometres under extreme conditions. [...] Read more.
A car cabin’s small volume makes it vulnerable to discomfort if temperature, humidity, and carbon dioxide levels are poorly regulated. In electric vehicles, the HVAC system draws energy from the car battery, reducing the driving range by several dozen kilometres under extreme conditions. A 1D simulation model calibrated for the Renault ZOE was used to evaluate the effects of ventilation parameters on thermal comfort, humidity, and power consumption. The results highlighted the interdependence of factors such as the recirculation ratio and blower flow rate, showing that energy-efficient settings depend on ambient conditions and other factors (such as occupancy, vehicle speed, infiltration). Adjustments can reduce heat pump energy use, but no single setting optimally balances power consumption and thermal comfort across all scenarios. The opti-CO2 mode is proposed as a trade-off, offering energy savings while maintaining safety and comfort. This mode quickly achieves the cabin temperature target, limits carbon dioxide concentration at a safe level (1100 ppm), minimises fogging risks, and reduces heat pump power consumption. Compared to fresh air mode, the opti-CO2 mode extends the driving range by 9 km in cold conditions and 26 km in hot conditions, highlighting its potential for improving energy efficiency and occupant comfort in electric vehicles. Full article
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<p>Overview of airflows in a vehicle cabin.</p>
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<p>The effect of CO<sub>2</sub> on human decision-making performance, adapted from [<a href="#B19-wevj-16-00098" class="html-bibr">19</a>].</p>
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<p>Renault ZOE reversible heat pump, adapted from [<a href="#B24-wevj-16-00098" class="html-bibr">24</a>].</p>
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<p>Overview of the complete simulation model in GT-Suite.</p>
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<p>Modelling the energy balance of a car cabin in GT-Suite [<a href="#B25-wevj-16-00098" class="html-bibr">25</a>].</p>
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<p>Input data for the simulation (supply air flow rate and recirculation ratio).</p>
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<p>A comparison between the experiment and simulation of a cabin temperature rise in a cold climate.</p>
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<p>Airflow repartition for the five recirculation flap configurations of the COLD scenario.</p>
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<p>Cabin CO<sub>2</sub> concentration for the five configurations of the COLD scenario.</p>
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<p>Temperature curves of the COLD scenario (<b>left</b>) and for the opti-CO<sub>2</sub> configuration (<b>right</b>).</p>
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<p>Relative humidity (<b>top row</b>) and absolute humidity (<b>bottom row</b>) on the three scenarios.</p>
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<p>Condensation formation on glass surfaces (cabin side) in the 0% fresh air configuration of the COLD scenario.</p>
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<p>The power consumption needed for thermal comfort on three ambient scenarios and the impact on the driving range of an electrical vehicle.</p>
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<p>The power consumption (of compressor heat pump) needed for thermal comfort and the impact on the driving range of an electrical vehicle.</p>
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<p>Infiltration volumetric flow rate for the Renault ZOE and the Peugeot 208, with a blower flow rate of 200 m<sup>3</sup>/h.</p>
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<p>The power consumption of the compressor and driving range gain on three ambient scenarios, depending on the infiltration model.</p>
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<p>The volume of infiltration air on WLTC, for different ventilation configurations.</p>
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<p>The steady-state recirculation ratio to maintain the cabin CO<sub>2</sub> concentration at 1100 ppm.</p>
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<p>The power consumption of the heat pump on WLTC in opti-CO<sub>2</sub> mode for three blower flow rates.</p>
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<p>The power consumption of the heat pump on WLTC for different blower flow rates.</p>
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<p>A comparison of the three blower flow rates with the HOT scenario—100% fresh air case.</p>
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21 pages, 1867 KiB  
Article
Deployment of TinyML-Based Stress Classification Using Computational Constrained Health Wearable
by Asma Abu-Samah, Dalilah Ghaffa, Nor Fadzilah Abdullah, Noorfazila Kamal, Rosdiadee Nordin, Jennifer C. Dela Cruz, Glenn V. Magwili and Reginald Juan Mercado
Electronics 2025, 14(4), 687; https://doi.org/10.3390/electronics14040687 - 10 Feb 2025
Abstract
Stress has become a common mental health issue in modern society, causing individuals to experience acute behavioral changes. Exposure to prolonged stress without proper prevention and treatment may cause severe damage to one’s physiological and psychological health. Researchers around the world have been [...] Read more.
Stress has become a common mental health issue in modern society, causing individuals to experience acute behavioral changes. Exposure to prolonged stress without proper prevention and treatment may cause severe damage to one’s physiological and psychological health. Researchers around the world have been working to find and create solutions for early stress detection using machine learning (ML). This paper investigates the possibility of utilizing Tiny Machine Learning (TinyML) in developing a wearable device, comparable to a smartwatch, that is equipped with both physiological and psychological data detection system to enable edge computing and give immediate feedback for stress prediction. The main challenge of this study was to fit a trained ML model into the microcontroller’s limited memory without compromising the model’s accuracy. A TinyML-based framework using a Raspberry Pi Pico RP2040 on a customized board equipped with several health sensors was proposed to predict stress levels by utilizing accelerations, body temperature, heart rate, and electrodermal activity from a public health dataset. Moreover, a few selected machine learning models underwent hyperparameter tuning before a porting library was used to translate them from Python to C/C++ for deployment. This approach led to an optimized XGBoost model with 86.0% accuracy and only 1.12 MB in size, hence perfectly fitting into the 2 MB constraint of RP2040. The prediction of stress on the edge device was then tested and validated using a separate sub-dataset. This trained model on TinyML can also be used to obtain an immediate reading from the calibrated health sensors for real-time stress predictions. Full article
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<p>The complete proposed workflow accompanied by an image of the edge device prototype.</p>
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<p>Data distribution of each feature.</p>
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<p>Original class distribution of the nurse dataset.</p>
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<p>Class distribution before and after NearMiss-1 undersampling.</p>
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<p>Confusion matrix of the six classifier models: (<b>a</b>) K-Nearest Neighbour. (<b>b</b>) XGBoost. (<b>c</b>) Random Forest. (<b>d</b>) Decision Tree. (<b>e</b>) LightGBM. (<b>f</b>) Logistic Regression.</p>
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<p>XGBoost(3) individual decision trees.</p>
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<p>A zoom in of <a href="#electronics-14-00687-f0A1" class="html-fig">Figure A1</a> on one of the XGBoost branch (or parallel tree) in making decision.</p>
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<p>Some part of the conditional statements based decision tree of XGBoost(3) when being translated into C/C++ code.</p>
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12 pages, 2975 KiB  
Article
Passive Resistance Network Temperature Compensation for Piezo-Resistive Pressure Sensors
by Cheng Lei, Yuqiao Liu, Ting Liang, Mengxuan Tang, Abdul Ghaffar and Sayed Hyder Abbas Musavi
Electronics 2025, 14(4), 653; https://doi.org/10.3390/electronics14040653 - 8 Feb 2025
Abstract
The operating temperature can significantly affect the output voltage of high-temperature piezoresistive pressure sensors, presenting challenges to the measurement precision due to the intrinsic properties of semiconductor materials. This study developed a passive resistor network temperature compensation technique, utilizing differential equations to determine [...] Read more.
The operating temperature can significantly affect the output voltage of high-temperature piezoresistive pressure sensors, presenting challenges to the measurement precision due to the intrinsic properties of semiconductor materials. This study developed a passive resistor network temperature compensation technique, utilizing differential equations to determine the compensation resistance parameters. Unlike conventional empirical algorithms, this method eliminated the need to account for variations among piezoresistors and addressed issues such as residual stress and mismatched coefficients of thermal expansion arising during manufacturing. The differential equation was simplified to derive the solution, and the calibration data were utilized to calculate the compensation resistance parameters, effectively compensating for the high-temperature piezoresistive pressure sensor. The results indicated that the passive resistance network successfully reduced the temperature drift, outperforming the traditional empirical algorithms. Full article
(This article belongs to the Special Issue New Insights Into Smart and Intelligent Sensors)
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<p>Bridge zero compensation serial connection.</p>
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<p>Bridge zero compensation parallel connection.</p>
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<p>Bridge sensitivity compensation connection. (<b>a</b>) Temperature drift compensation of bridge sensitivity powered by constant voltage source; (<b>b</b>) Temperature drift compensation of bridge sensitivity supplied by constant current source.</p>
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<p>Conventional low-temperature coefficient resistor network compensation circuit.</p>
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<p>Temperature compensation model of passive resistor network powered by constant voltage source. (<b>a</b>) Compensation model of sensor with negative initial zero voltage at room temperature; (<b>b</b>) Compensation model of sensor with positive initial zero voltage at room temperature.</p>
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<p>Temperature and pressure calibration of uncompensated pressure sensitive chip unit.</p>
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<p>Temperature compensated calibration of conventional low-temperature coefficient resistance network.</p>
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<p>Temperature compensation circuit of passive resistor network.</p>
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<p>Temperature compensated calibration of passive resistor network.</p>
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14 pages, 1583 KiB  
Article
Thermodynamic Model of a Gas Turbine Considering Atmospheric Conditions and Position of the IGVs
by Tarik Boushaki and Kacem Mansouri
Thermo 2025, 5(1), 5; https://doi.org/10.3390/thermo5010005 - 7 Feb 2025
Abstract
Gas turbines are widely used in power generation due to their efficiency, flexibility, and low environmental impact. Modeling, especially in thermodynamics, is crucial for the designer and operator of a gas turbine. An advanced and rigorous thermodynamic model is essential to accurately predict [...] Read more.
Gas turbines are widely used in power generation due to their efficiency, flexibility, and low environmental impact. Modeling, especially in thermodynamics, is crucial for the designer and operator of a gas turbine. An advanced and rigorous thermodynamic model is essential to accurately predict the performance of a gas turbine under on-design operating conditions, off-design or failure. Such models not only improve understanding of internal processes but also optimize performance and reliability in a wide variety of operational scenarios. This article presents the development of a thermodynamic model simulating the off-design performance of a gas turbine. The mathematical relationships established in this model allow for quick calculations while requiring a limited amount of data. Only nominal data are required, and some additional data are needed to calibrate the model on the turbine under study. A key feature of this model is the development of an innovative relationship that allows direct calculation of the mass flow of air entering the turbine and, thus, the performances of the turbine according to atmospheric conditions (such as pressure, temperature, and relative humidity) and the position of the compressor inlet guide vanes (IGV). The results of the simulations, obtained using code implemented in MATLAB (R2014a), demonstrate the efficiency of the model compared to experimental data. Indeed, the model relationships exhibit high determination coefficients (R2 > 0.95) and low root mean square errors (RMSE). Specifically, the simulation results for the air mass flow rate demonstrate a very high determination coefficient (R2 = 0.9796) and a low root mean square error (RMSE = 0.0213). Full article
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<p>Schematic diagram of the main elements of a gas-combined cycle.</p>
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<p>Corrected mass flow rate of air entering the compressor for different atmospheric conditions and IGV opening angles.</p>
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<p>Calculation precision of the corrected mass flow rate for different operating states of the compressor.</p>
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<p>Compression ratio calculated and measured for different states of its operation.</p>
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<p>Gas turbine performance as a function of ambient temperature.</p>
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20 pages, 4674 KiB  
Article
Investigating the Zonal Response of Spatiotemporal Dynamics of Australian Grasslands to Ongoing Climate Change
by Jingai Bai and Tingbao Xu
Land 2025, 14(2), 296; https://doi.org/10.3390/land14020296 - 31 Jan 2025
Abstract
Grasslands are key components of land ecosystems, providing valuable ecosystem services and contributing to local carbon sequestration. Australian grasslands, covering approximately 70% of the continent, are vital for agriculture, pasture, and ecosystem services. Ongoing climate change introduces considerable uncertainties about the dynamic responses [...] Read more.
Grasslands are key components of land ecosystems, providing valuable ecosystem services and contributing to local carbon sequestration. Australian grasslands, covering approximately 70% of the continent, are vital for agriculture, pasture, and ecosystem services. Ongoing climate change introduces considerable uncertainties about the dynamic responses of different types of grasslands to changes in regional climate and its variation. This study, bringing together high-resolution meteorological data, calibrated long-term satellite NDVI data, and NPP and statistical models, investigated the spatiotemporal variability of NDVI and NPP and their predominant drivers (temperature and soil water content) across Australia’s grassland zones from 1992 to 2021. Results showed a slight, non-significant NDVI increase, primarily driven by improved vegetation in northern savannah grasslands (SGs). Areal average annual NPP values fluctuated annually but with a levelled trend over time, illustrating grassland resilience. NDVI and NPP measures aligned spatially, with values decreasing from the coastal to the inland regions and north to south. Most of the SGs experienced an increase in NDVI and NPP, boosted by abundant soil moisture and warm weather, which promoted vegetation growth and sustained a stable growing biomass in this zone. The increased NDVI and NPP in northern open grasslands (OGs) were linked to wetter conditions, while their decreases in western desert grasslands (DGs) were ascribed to warming and drier weather. Soil water availability was the dominant driver of grassland growth, with NDVI being positively correlated with soil water content but being negatively correlated with temperature across most grasslands. Projections under the SSP126 and SSP370 scenarios using ACCESS-ESM1.5 showed slight NPP increases by 2050 under warmer and wetter conditions, though western and southern grasslands may see declines in vegetation coverage and carbon storage. This study provides insights into the responses of Australian grasslands to climate variability. The results will help to underpin the design of sustainable grassland management strategies and practices under a changing climate for Australia. Full article
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<p>The study area, three major grassland zones in Australia.</p>
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<p>The flow chart of this study.</p>
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<p>Interannual variations in climate conditions of Australian grasslands and their three zones from 1992 to 2021: MATs (<b>a</b>,<b>b</b>) and SWC (<b>c</b>,<b>d</b>).</p>
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<p>Spatial distributions of annual averages and trends of MATs (<b>a</b>,<b>b</b>) and SWC (<b>c</b>,<b>d</b>) across Australian grasslands from 1992 to 2021. The black contours represent the three grassland zones: SGs, OGs, and DGs, respectively, from north to south. The grey area refers to non-study areas.</p>
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<p>Interannual variations in vegetation condition indicators of Australian grasslands and their three grassland zones from 1992 to 2021: NDVI (<b>a</b>,<b>b</b>) and NPP (<b>c</b>,<b>d</b>).</p>
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<p>Spatial distributions of annual averages and trends of NDVI (<b>a</b>,<b>b</b>) and NPP (<b>c</b>,<b>d</b>) of Australian grasslands from 1992 to 2021.</p>
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<p>The spatial correlation coefficients between annual NDVI and temperature (<b>a</b>) and soil water content (<b>b</b>) in Australian grasslands from 1992 to 2021.</p>
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<p>Spatial distributions of projected temperatures across Australian grasslands for 2050 under the SSP126 (<b>a</b>) and SSP370 (<b>c</b>) climate scenarios, as simulated by ACCESS-ESM1.5 in CMIP6. The spatial distribution of temperature differentials between the projected 2050 temperatures and the MATs during 1992–2021 is shown for SSP126 (<b>b</b>) and SSP370 (<b>d</b>) climate scenarios.</p>
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<p>Spatial distributions of projected temperatures across Australian grasslands for 2050 under the SSP126 (<b>a</b>) and SSP370 (<b>c</b>) climate scenarios, as simulated by ACCESS-ESM1.5 in CMIP6. The spatial distribution of rainfall differentials between the projected 2050 rainfall and the rainfall during 1992–2021 is shown for SSP126 (<b>b</b>) and SSP370 (<b>d</b>) climate scenarios.</p>
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<p>Spatial distributions of projected NPP across Australian grasslands for 2050 under the SSP126 (<b>a</b>) and SSP370 (<b>c</b>) climate scenarios, as simulated by ACCESS-ESM1.5 in CMIP6. The spatial distribution of NPP differentials between the projected 2050 NPP and the mean annual NPP during 1992–2021 is shown for SSP126 (<b>b</b>) and SSP370 (<b>d</b>) climate scenarios.</p>
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17 pages, 5985 KiB  
Article
A Highly Spatiotemporal Resolved Pyrometry for Combustion Temperature Measurement of Single Microparticles Applied in Powder-Fueled Ramjets
by Zhangtao Wang, Xunjie Lin, Xuefeng Huang, Houye Huang, Minqi Zhang, Qinnan Yu, Chao Cui and Shengji Li
Nanomaterials 2025, 15(3), 223; https://doi.org/10.3390/nano15030223 - 30 Jan 2025
Abstract
It is vital to measure combustion temperature to define combustion models accurately. For single fuel particles in powder-fueled ramjets, their size distribution ranges from submicron to submillimeter, and their burn time is short to millisecond order. Moreover, the radiation intensity of different types [...] Read more.
It is vital to measure combustion temperature to define combustion models accurately. For single fuel particles in powder-fueled ramjets, their size distribution ranges from submicron to submillimeter, and their burn time is short to millisecond order. Moreover, the radiation intensity of different types of fuel particles significantly oscillated with several orders of magnitude. Current temperature measurement technology is facing this challenge. This paper proposes a highly spatiotemporal resolved pyrometry to measure the combustion temperature of fuel particles by coupling single-point photomultiplier tube (PMT)-based and two-dimensional complementary metal oxide semiconductor (CMOS)-based photoelectric devices. Both the offline calibration by blackbody furnace and online calibration by standard lamp confirmed the measurement accuracy of the pyrometry. Then, the pyrometry was used to measure the combustion temperature of fuel particles including micro-Al, nano-Al, micro-Mg, nano-B, and micro-B4C. The temperature evolution and distribution of burning fuel particles were complementarily obtained, especially the interfacial flame temperature near the particle surface. Based on the obtained combustion temperature, the combustion characteristics and the energy release efficiencies among these fuels were evaluated and compared in detail, which are helpful to recognize, in depth, the combustion behavior and reveal the combustion mechanism of fuel particles in powder-fueled ramjets. Full article
(This article belongs to the Special Issue Advances in Nano-Enhanced Thermal Functional Materials)
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<p>Schematic of experimental setup for ignition and combustion characterization (<b>a</b>) and PMT-based three-color pyrometry (<b>b</b>).</p>
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<p>The offline calibrated temperature curves of independent PMT-based pyrometry (<b>a</b>) and CMOS-based pyrometry (<b>b</b>) via a blackbody furnace.</p>
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<p>The online calibrated temperature curves of coupled PMT&amp;CMOS pyrometry (<b>a</b>) and measurement errors (<b>b</b>) via a standard lamp with 2856 K color temperature.</p>
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<p>(<b>a</b>) A snapshot of a two-dimensional flame image of a single isolated micro-Al particle burned in air and (<b>b</b>) corresponding two-dimensional temperature distribution via coupled PMT&amp;CMOS pyrometry.</p>
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<p>A sequence of two-dimensional flame images and corresponding temperature distribution of the single isolated micro-Al particle burned in air (10,000 fps, exposure time of 99 μs), (<b>a1</b>,<b>a2</b>) 0.6 ms, (<b>b1</b>,<b>b2</b>) 1.6 ms, (<b>c1</b>,<b>c2</b>) 2.6 ms, (<b>d1</b>,<b>d2</b>) 3.6 ms, (<b>e1</b>,<b>e2</b>) 4.6 ms, (<b>f1</b>,<b>f2</b>) 5.4 ms.</p>
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<p>The PMT-based and CMOS-based temperature curves of the single micro-Al particle burned in air via coupled PMT&amp;CMOS pyrometry.</p>
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<p>A sequence of two-dimensional flame images and corresponding temperature distribution of a nano-Al aggregate burned in air (11,000 fps, exposure time of 90 μs), (<b>a1</b>,<b>a2</b>) 0.18 ms, (<b>b1</b>,<b>b2</b>) 0.54 ms, (<b>c1</b>,<b>c2</b>) 0.91 ms, (<b>d1</b>,<b>d2</b>) 1.27 ms, (<b>e1</b>,<b>e2</b>) 1.64 ms, (<b>f1</b>,<b>f2</b>) 2.36 ms.</p>
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<p>The PMT-based and CMOS-based temperature curves of the nano-Al fuel aggregate burned in air via coupled PMT&amp;CMOS pyrometry.</p>
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<p>(<b>a</b>) The image of a single isolated micro-Mg particle, (<b>b</b>) a snapshot of the two-dimensional flame image of the micro-Mg particle burned in air, and (<b>c</b>) the corresponding two-dimensional temperature distribution via coupled PMT&amp;CMOS pyrometry (10,000 fps, exposure time of 5 μs).</p>
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<p>The PMT-based temperature curve of the single isolated micro-Mg particle burned in air via coupled PMT&amp;CMOS pyrometry.</p>
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<p>(<b>a</b>) A snapshot of the two-dimensional flame image of a single isolated nano-B aggregate burned in air, and (<b>b</b>) corresponding two-dimensional temperature distribution via coupled PMT&amp;CMOS pyrometry (10,000 fps, exposure time of 99 μs).</p>
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<p>A sequence of two-dimensional flame images and the corresponding temperature distribution of the single isolated nano-B aggregate burned in air (10,000 fps, exposure time of 99 μs), (<b>a1</b>,<b>a2</b>) 0.0 ms, (<b>b1</b>,<b>b2</b>) 5.0 ms, (<b>c1</b>,<b>c2</b>) 10.0 ms, (<b>d1</b>,<b>d2</b>) 15.0 ms, (<b>e1</b>,<b>e2</b>) 20.0 ms, (<b>f1</b>,<b>f2</b>) 25.0 ms.</p>
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<p>The PMT-based and CMOS-based temperature curves of the single isolated nano-B aggregate burned in air via coupled PMT&amp;CMOS pyrometry.</p>
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<p>A sequence of snapshots of two-dimensional flame images and the corresponding temperature distribution of a single isolated micro-B<sub>4</sub>C particle burned in air (10,000 fps, exposure time of 50 μs), (<b>a1</b>,<b>a2</b>) 0.0 ms, (<b>b1</b>,<b>b2</b>) 2.0 ms, (<b>c1</b>,<b>c2</b>) 4.0 ms, (<b>d1</b>,<b>d2</b>) 6.0 ms, (<b>e1</b>,<b>e2</b>) 8.0 ms.</p>
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<p>The PMT-based and CMOS-based temperature curves of a single isolated micro-B<sub>4</sub>C particle burned in air via coupled PMT&amp;CMOS pyrometry.</p>
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16 pages, 1686 KiB  
Article
Trace Detection of Di-Isopropyl Methyl Phosphonate DIMP, a By-Product, Precursor, and Simulant of Sarin, Using Either Ion Mobility Spectrometry or GC-MS
by Victor Bocoș-Bințințan, Paul-Flaviu Bocoș-Bințințan, Tomáš Rozsypal and Mihail Simion Beldean-Galea
Toxics 2025, 13(2), 102; https://doi.org/10.3390/toxics13020102 - 28 Jan 2025
Abstract
Di-isopropyl methyl phosphonate (DIMP) has no major commercial uses but is a by-product or a precursor in the synthesis of the nerve agent sarin (GB). Also, DIMP is utilized as a simulant compound for the chemical warfare agents sarin and soman in order [...] Read more.
Di-isopropyl methyl phosphonate (DIMP) has no major commercial uses but is a by-product or a precursor in the synthesis of the nerve agent sarin (GB). Also, DIMP is utilized as a simulant compound for the chemical warfare agents sarin and soman in order to test and calibrate sensitive IMS instrumentation that warns against the deadly chemical weapons. DIMP was measured from 2 ppbv (15 μg m−3) to 500 ppbv in the air using a pocket-held ToF ion mobility spectrometer, model LCD-3.2E, with a non-radioactive ionization source and ammonia doping in positive ion mode. Excellent sensitivity (LoD of 0.24 ppbv and LoQ of 0.80 ppbv) was noticed; the linear response was up to 10 ppbv, while saturation occurred at >500 ppbv. DIMP identification by IMS relies on the formation of two distinct peaks: the monomer M·NH4+, with a reduced ion mobility K0 = 1.41 cm2 V−1 s−1, and the dimer M2·NH4+, with K0 = 1.04 cm2 V−1 s−1 (where M is the DIMP molecule); positive reactant ions (Pos RIP) have K0 = 2.31 cm2 V−1 s−1. Quantification of DIMP at trace levels was also achieved by GC-MS over the concentration range of 1.5 to 150 μg mL−1; using a capillary column (30 m × 0.25 mm × 0.25 μm) with a TG-5 SilMS stationary phase and temperature programming from 60 to 110 °C, DIMP retention time (RT) was ca. 8.5 min. The lowest amount of DIMP measured by GC-MS was 1.5 ng, with an LoD of 0.21 μg mL−1 and an LoQ of 0.62 μg mL−1 DIMP. Our results demonstrate that these methods provide robust tools for both on-site and off-site detection and quantification of DIMP at trace levels, a finding which has significant implications for forensic investigations of chemical agent use and for environmental monitoring of contamination by organophosphorus compounds. Full article
(This article belongs to the Section Drugs Toxicity)
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<p>Ion mobility spectra from DIMP, obtained in the positive ion mode.</p>
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<p>Calibration for DIMP, in the positive ion mode.</p>
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<p>Chromatogram for DIMP—1 μL of 60 mg mL<sup>−1</sup>, splitless. Note the peak of the impurity tri-isopropyl phosphate (TIPP) at a retention time of ca. 12 min.</p>
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<p>Chromatogram and mass spectrum for DIMP, for an on-column mass of 13.64 ng (split ratio 10:1).</p>
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