[go: up one dir, main page]
More Web Proxy on the site http://driver.im/ Skip to main content
Springer Nature Link
Log in

Artificial neural network prediction of TiO2-doped chitosan micro/nanoparticle size based on particle imaging measurements

  • Research
  • Published:
Colloid and Polymer Science Aims and scope Submit manuscript

Abstract

In this study, TiO2-doped chitosan micro/nanoparticles were fabricated using the ionic gelation mechanism under several process parameters to exhibit the strategy of introducing particle image data for the prediction of particle size. Herein, we report on a detailed methodology for the prediction of prepared particles via artificial neural network (ANN) algorithm using the multi-layer perceptron (MLP) and radial basis function (RBF) models to select the model that demonstrates the best performance for estimation of particle size. Chitosan and TiO2-doped chitosan micro/nanoparticles were imaged, processed, and analyzed as particle diameters in order to explore prediction models, which were developed under three different classes of prepared particles (chitosan, TiO2-doped chitosan, and chitosan/TiO2-doped chitosan). Models were built using particle fabrication process parameters as input with particle size as output. The established MLP model successfully predicted the particle size of all classes with the mean square error (MSE) and correlation coefficient (R) between the observed and predicted values in the range of 0.0012–0.0065 and 0.85–0.90, respectively. The best results for prediction were achieved from the RBF model for all classes of particles where MSE and R values were determined as 2.93 × 10−22–4.93 × 10−11 and 1.0, respectively. Results successfully highlighted the prediction process of particle sizes via MLP and RBF models could be relevant in the decision to produce TiO2-doped chitosan particles and confirmed the usefulness of particle image data for simulation.

Graphical Abstract

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
£29.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price includes VAT (United Kingdom)

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

Data availability

The TiO2-doped chitosan particle data supporting this article have been included as part of the Supplementary Information, and all other data are available on request from the authors.

Abbreviations

ANN:

Artificial neural network

DLS:

Dynamic light scattering

FTIR:

Fourier transform infrared

MAE:

Mean absolute error

MLP:

Multi-layer perceptron

MSE:

Mean square error

PCL:

Poly(caprolactone)

PEG:

Polyethylene glycol

PLA:

Polylactide

PLGA:

Poly(lactic-co-glycolic acid)

PVA:

Poly(vinyl acetate)

R:

Correlation coefficient

R2 :

Coefficient of determination

RBF:

Radial basis function

RMSE:

Root mean square error

TANSIG:

Tangent sigmoid

TiO2 :

Titanium dioxide

TPP:

Sodium tripolyphosphate

XRD:

X-ray diffraction

\({\upmu }_{\mathrm{j}}\) :

RBF function center

\({\upsigma }_{j}\) :

The spread of the Gaussian basis function

\({\upphi }_{\mathrm{j}}\left(\mathrm{x}\right)\) :

The nonlinear function of unit j

\({\mathrm{x}}_{0}\) :

Activation of the hidden layer node

\({\mathrm{x}}_{\mathrm{n}}\) :

Activation of nth hidden layer node

\(\overline{y}i\) :

The mean of the target values

\({\upomega }_{\mathrm{no}}\) :

The interconnection between the nth hidden layer node and the oth output layer node

f():

Activation function

\(\widehat{y}i\) :

Network outputs (predicted)

\(yi\) :

Targets (observed)

References

  1. Raut AV et al (2016) Synthesis and characterization of chitosan-TiO: Cu nanocomposite and their enhanced antimicrobial activity with visible light. Colloids Surfaces B-Biointerfaces 148:566–575

    Article  PubMed  CAS  Google Scholar 

  2. Andres Y et al (2007) Antibacterial effects of chitosan powder: mechanisms of action. Environ Technol 28(12):1357–1363

    Article  PubMed  CAS  Google Scholar 

  3. Sanpui P et al (2008) The antibacterial properties of a novel chitosan-Ag-nanoparticle composite. Int J Food Microbiol 124(2):142–146

    Article  PubMed  CAS  Google Scholar 

  4. Dai TH et al (2013) Chitosan preparations for wounds and burns: antimicrobial and wound-healing effects (vol 9, pg 857, 2011). Expert Rev Anti-Infect Ther 11(8):866–866

    CAS  Google Scholar 

  5. Manni L et al (2010) Extraction and characterization of chitin, chitosan, and protein hydrolysates prepared from shrimp waste by treatment with crude protease from Bacillus cereus SV1. Appl Biochem Biotechnol 162(2):345–357

    Article  PubMed  CAS  Google Scholar 

  6. Arain RA et al (2013) Antibacterial property and characterization of cotton fabric treated with chitosan/AgCl-TiO colloid. Carbohyd Polym 96(1):326–331

    Article  CAS  Google Scholar 

  7. Zhang XD et al (2017) Preparation of chitosan-TiO composite film with efficient antimicrobial activities under visible light for food packaging applications. Carbohyd Polym 169:101–107

    Article  CAS  Google Scholar 

  8. Li B et al (2016) Synthesis, characterization, and antibacterial activity of chitosan/TiO nanocomposite against pv. Carbohyd Polym 152:825–831

    Article  CAS  Google Scholar 

  9. Qian TT et al (2011) The bactericidal and mildew-proof activity of a TiO-chitosan composite. J Photochem Photobiol A-Chem 218(1):130–136

    Article  CAS  Google Scholar 

  10. Chen XY et al (2013) Fabrication of selenium-deposited and chitosan-coated titania nanotubes with anticancer and antibacterial properties. Colloids Surfaces B-Biointerfaces 103:149–157

    Article  PubMed  CAS  Google Scholar 

  11. Bard AJ (1994) Integrated chemical systems: a chemical approach to nanotechnology, Wiley, p 342

  12. Xing Y et al (2020) Effects of different TiO(2) nanoparticles concentrations on the physical and antibacterial activities of chitosan-based coating film. Nanomaterials (Basel) 10(7):1365

    Article  PubMed  CAS  Google Scholar 

  13. He CB et al (2012) Size-dependent absorption mechanism of polymeric nanoparticles for oral delivery of protein drugs. Biomaterials 33(33):8569–8578

    Article  PubMed  CAS  Google Scholar 

  14. Peer D et al (2007) Nanocarriers as an emerging platform for cancer therapy. Nat Nanotechnol 2(12):751–760

    Article  PubMed  CAS  Google Scholar 

  15. Esmaeilzadeh-Gharedaghi E et al (2012) Effects of processing parameters on particle size of ultrasound prepared chitosan nanoparticles: an artificial neural networks study. Pharm Dev Technol 17(5):638–647

    Article  PubMed  CAS  Google Scholar 

  16. Tao HC et al (2021) Nanoparticle synthesis assisted by machine learning. Nat Rev Mater 6(8):701–716

    Article  Google Scholar 

  17. Youshia J et al (2017) Artificial neural network based particle size prediction of polymeric nanoparticles. Eur J Pharm Biopharm 119:333–342

    Article  PubMed  CAS  Google Scholar 

  18. Shalaby KS et al (2014) Determination of factors controlling the particle size and entrapment efficiency of noscapine in PEG/PLA nanoparticles using artificial neural networks. Int J Nanomed 9:4953–4964

    CAS  Google Scholar 

  19. Hashad RA et al (2016) Chitosan-tripolyphosphate nanoparticles: optimization of formulation parameters for improving process yield at a novel pH using artificial neural networks. Int J Biol Macromol 86:50–58

    Article  PubMed  CAS  Google Scholar 

  20. Bozuyuk U et al (2018) Deep insight into PEGylation of bioadhesive chitosan nanoparticles: sensitivity study for the key parameters through artificial neural network model. ACS Appl Mater Interfaces 10(40):33945–33955

    Article  PubMed  CAS  Google Scholar 

  21. Jia ZX et al (2023) Dynamic light scattering: a powerful tool for in situ nanoparticle sizing. Colloids and Interfaces 7(1):15

    Article  CAS  Google Scholar 

  22. Aydin RST, Pulat M (2012) 5-fluorouracil encapsulated chitosan nanoparticles for pH-stimulated drug delivery: evaluation of controlled release kinetics. J Nanomater 2012(1):313961

    Article  Google Scholar 

  23. Aydin RST, Kazanci F (2018) Synthesis and characterization of ozonated oil nanoemulsions. J Am Oil Chem Soc 95(11):1385–1398

    Article  Google Scholar 

  24. Pham BT et al (2019) A novel artificial intelligence approach based on multi-layer perceptron neural network and biogeography-based optimization for predicting coefficient of consolidation of soil. Catena 173:302–311

    Article  Google Scholar 

  25. Kashaninejad M et al (2009) Modeling of wheat soaking using two artificial neural networks (MLP and RBF). J Food Eng 91(4):602–607

    Article  Google Scholar 

  26. Yilmaz I, Kaynar O (2011) Multiple regression, ANN (RBF, MLP) and ANFIS models for prediction of swell potential of clayey soils. Expert Syst Appl 38(5):5958–5966

    Article  Google Scholar 

  27. Kalantary S et al (2019) Application of ANN modeling techniques in the prediction of the diameter of PCL/gelatin nanofibers in environmental and medical studies. RSC Adv 9(43):24858–24874

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  28. Jahani A, Fazel A (2016) Aesthetic quality modeling of landscape in urban greenspace by using artificial neural network. J Nat Environ 69:951–963

    Google Scholar 

  29. Khanlou HM et al (2014) Prediction and optimization of electrospinning parameters for polymethyl methacrylate nanofiber fabrication using response surface methodology and artificial neural networks. Neural Comput Appl 25(3):767–777

    Article  Google Scholar 

  30. Foody GM (2004) Supervised image classification by MLP and RBF neural networks with and without an exhaustively defined set of classes. Int J Remote Sens 25(15):3091–3104

    Article  Google Scholar 

  31. Qi LF et al (2004) Preparation and antibacterial activity of chitosan nanoparticles. Carbohyd Res 339(16):2693–2700

    Article  CAS  Google Scholar 

  32. Nasti A et al (2009) Chitosan/TPP and chitosan/TPP-hyaluronic acid nanoparticles: systematic optimisation of the preparative process and preliminary biological evaluation. Pharm Res 26(8):1918–1930

    Article  PubMed  CAS  Google Scholar 

  33. Oudih SB et al (2023) Chitosan nanoparticles with controlled size and zeta potential. Polym Eng Sci 63(3):1011–1021

    Article  Google Scholar 

  34. Sawtarie N et al (2017) Preparation of chitosan/tripolyphosphate nanoparticles with highly tunable size and low polydispersity. Colloids Surf, B 157:110–117

    Article  CAS  Google Scholar 

  35. Anaya-Esparza LM et al (2020) Chitosan-TiO: a versatile hybrid composite. Materials 13(4):811

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  36. Spoiala A et al (2022) Preparation and characterization of chitosan/TiO composite membranes as adsorbent materials for water purification. Membranes 12(8):804

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  37. Deveci I et al (2015) Synthesis and characterization of chitosan/TiO2 composite beads for improving stability of porcine pancreatic lipase. Appl Biochem Biotechnol 175(2):1052–1068

    Article  PubMed  CAS  Google Scholar 

  38. Al-Taweel S et al (2019) The influence of titanium dioxide nanofiller ratio on morphology and surface properties of TiO2/Chitosan nanocomposite. Results Phys 13:102296

    Article  Google Scholar 

  39. Díaz-Visurraga J et al (2010) Semitransparent chitosan-TiO nanotubes composite film for food package applications. J Appl Polym Sci 116(6):3503–3515

    Article  Google Scholar 

  40. Siripatrawan U, Kaewklin P (2018) Fabrication and characterization of chitosan-titanium dioxide nanocomposite film as ethylene scavenging and antimicrobial active food packaging. Food Hydrocoll 84:125–134

    Article  CAS  Google Scholar 

  41. Kadhum A (2017) Antimicrobial activity of TiO2 NPs against Escherichia coli ATCC 25922 and Staphylococcus aureus ATCC 25923. Int J Comput Appl Sci IJOCAAS 2:6–10

    Google Scholar 

  42. McGarry K et al (2001) Knowledge extraction from local function networks. Proceedings of the International Joint Conference on Artificial Intelligence, Seattle, US

  43. Baharifar H, Amani A (2017) Size, Loading efficiency, and cytotoxicity of albumin-loaded chitosan nanoparticles: an artificial neural networks study. J Pharm Sci 106(1):411–417

    Article  PubMed  CAS  Google Scholar 

Download references

Acknowledgements

The authors acknowledge Dr. Ceren Kaya for her technical support in designing ANN architecture.

Funding

This work has been supported by funding from the Department of the Scientific Research Projects at Zonguldak Bülent Ecevit University (project no. 2021–39971044-04).

Author information

Authors and Affiliations

  1. Department of Biomedical Engineering, Zonguldak Bülent Ecevit University, Incivez, Zonguldak, 67100, Turkey

    R. Seda Tığlı Aydın & Aysu Demir

Authors
  1. R. Seda Tığlı Aydın
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Aysu Demir
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

A.D: Investigation, methodology, data curation, formal analysis and R.S.T.A: Conceptualization, investigation, funding acquisition, project administration, writing – review & editing.

Corresponding author

Correspondence to R. Seda Tığlı Aydın.

Ethics declarations

Ethical approval

Not applicable.

Competing interests

The authors declare no competing interests.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Below is the link to the electronic supplementary material.

Supplementary file1 (DOCX 38 KB)

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Aydın, R.S.T., Demir, A. Artificial neural network prediction of TiO2-doped chitosan micro/nanoparticle size based on particle imaging measurements. Colloid Polym Sci (2025). https://doi.org/10.1007/s00396-024-05368-2

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s00396-024-05368-2

Keywords

Search

Navigation