

This collective monograph explores recent advancements in chemical engineering with a focus on innovative materials and process optimization. Emphasizing interdisciplinary approaches, it highlights how modern computational methods contribute to the development of efficient, sustainable technologies. Authors examines the application of artificial neural networks in pharmaceutical research, demonstrating its potential in drug discovery. The volume aims to bridge the gap between emerging scientific tools and industrial practice, offering insights for researchers and engineers engaged in the development of next-generation chemical processes.
Chapter 1. Artificial neural networks in pharmaceutical research
Yaroslava Pushkarova, Galina Zaitseva
Chapter 2. Innovations in the development of water-soluble polymer systems for drug delivery
Olena Ishchenko, Olha Sumska , Volodymyr Bessarabov, Galyna Kuzmina, Liudmyla Vognivenko, Natalia Pelykh
Chapter 3. Research of a ZnO-based gas sensor for detection of hazardous chemicals at critical infrastructure facilities
Natalia Minska, Artem Huz, Volodymyr Kradozhon, Roman Melezhyk, Mykhailo Murin, Vasyl Rotar, Nataliia Tregub
PhD, Associate Professor
Department of Analytical, Physical and Colloid Chemistry
https://orcid.org/0000-0001-9856-7846
PhD, Associate Professor, Head of Department
Department of Analytical, Physical and Colloid Chemistry
https://orcid.org/0000-0003-3138-6324
Doctor of Technical Sciences, Professor
Department of Industrial Pharmacy
https://orcid.org/0000-0002-9510-6005
PhD, Associate Professor
Department of Food Technologies
https://orcid.org/0000-0003-1606-6103
Doctor of Technical Sciences, Professor
Department of Industrial Pharmacy
https://orcid.org/0000-0003-0637-1729
PhD, Associate Professor
Department of Industrial Pharmacy
https://orcid.org/0000-0002-0691-8563
PhD, Associate Professor
Department of Food Technologies
https://orcid.org/0000-0002-7866-8081
PhD, Associate Professor
Department of Technologies of Agricultural Production and Processing named after Academician V. G. Pelykh
https://orcid.org/0000-0003-4984-299X
Doctor of Technical Sciences, Associate Professor
Department of Radiation and Chemical Protection
https://orcid.org/0000-0001-8438-0618
Lecturer
Department of Organization and Technical Support of Emergency and Rescue Operations
https://orcid.org/0009-0004-8869-2423
Adjunct
Department of Automatic Safety Systems and Electrical Installations
https://orcid.org/0009-0004-1934-2120
Department of Organization of Scientific and Research Activities of the Scientific and Innovation Center
https://orcid.org/0000-0001-6425-4147
PhD, Associate Professor
Department of Automatic Safety Systems and Electrical Installations
https://orcid.org/0000-0002-9898-0128
PhD, Associate Professor
Department of Technics and Means of Civil Protection
https://orcid.org/0009-0006-5801-0959
PhD
Environment Design Department
Department of Design of Environment
https://orcid.org/0000-0002-4021-2601
Mak, K.-K., Wong, Y.-H., Pichika, M. R. (2024). Artificial Intelligence in Drug Discovery and Development. Drug Discovery and Evaluation: Safety and Pharmacokinetic Assays. Cham: Springer, 1461–1498. https://doi.org/10.1007/978-3-031-35529-5_92
Lu, M., Yin, J., Zhu, Q., Lin, G., Mou, M., Liu, F. et al. (2023). Artificial Intelligence in Pharmaceutical Sciences. Engineering, 27, 37–69. https://doi.org/10.1016/j.eng.2023.01.014
Pushkarova, Y., Kholin, Y. (2014). A procedure for meaningful unsupervised clustering and its application for solvent classification. Open Chemistry, 12 (5), 594–603. https://doi.org/10.2478/s11532-014-0514-6
Wu, Y., Feng, J. (2018). Development and Application of Artificial Neural Network. Wireless Personal Communications, 102 (2), 1645–1656. https://doi.org/10.1007/s11277-017-5224-x
Mishra, C., Gupta, D. L. (2017). Deep Machine Learning and Neural Networks: An Overview. IAES International Journal of Artificial Intelligence (IJ-AI), 6 (2), 66. https://doi.org/10.11591/ijai.v6.i2.pp66-73
Grossi, E., Buscema, M. (2007). Introduction to artificial neural networks. European Journal of Gastroenterology & Hepatology, 19 (12), 1046–1054. https://doi.org/10.1097/meg.0b013e3282f198a0
Zou, J., Han, Y., So, S.-S. (2008). Overview of Artificial Neural Networks. Artificial Neural Networks. Humana Press, 14–22. https://doi.org/10.1007/978-1-60327-101-1_2
Yanling, Z., Bimin, D., Zhanrong, W. (2002). Analysis and study of perceptron to solve XOR problem. The 2nd International Workshop on Autonomous Decentralized System. Beijing: IEEE, 168–173. https://doi.org/10.1109/iwads.2002.1194667
Anwar, S. M., Majid, M., Qayyum, A., Awais, M., Alnowami, M., Khan, M. K. (2018). Medical Image Analysis using Convolutional Neural Networks: A Review. Journal of Medical Systems, 42 (11). https://doi.org/10.1007/s10916-018-1088-1
Sarvamangala, D. R., Kulkarni, R. V. (2022). Convolutional neural networks in medical image understanding: a survey. Evolutionary Intelligence, 15 (1), 1–22. https://doi.org/10.1007/s12065-020-00540-3
Carpenter, K. A., Cohen, D. S., Jarrell, J. T., Huang, X. (2018). Deep Learning and Virtual Drug Screening. Future Medicinal Chemistry, 10 (21), 2557–2567. https://doi.org/10.4155/fmc-2018-031
Kimber, T. B., Chen, Y., Volkamer, A. (2021). Deep Learning in Virtual Screening: Recent Applications and Developments. International Journal of Molecular Sciences, 22 (9), 4435. https://doi.org/10.3390/ijms22094435
Moon, S., Zhung, W., Yang, S., Lim, J., Kim, W. Y. (2022). PIGNet: a physics-informed deep learning model toward generalized drug–target interaction predictions. Chemical Science, 13 (13), 3661–3673. https://doi.org/10.1039/d1sc06946b
Suresh, N., Chinnakonda Ashok Kumar, N., Subramanian, S., Srinivasa, G. (2022). Memory augmented recurrent neural networks for de-novo drug design. PLOS ONE, 17 (6), e0269461. https://doi.org/10.1371/journal.pone.0269461
Ruiz Puentes, P., Valderrama, N., González, C., Daza, L., Muñoz-Camargo, C., Cruz, J. C., Arbeláez, P. (2021). PharmaNet: Pharmaceutical discovery with deep recurrent neural networks. PLOS ONE, 16 (4), e0241728. https://doi.org/10.1371/journal.pone.0241728
Kaliuzhenko, A., Pushkarova, Y. (2023). Application of artificial neural networks for solving pharmaceutical issues. Grail of Science, 24, 766–769. https://doi.org/10.36074/grail-of-science.17.02.2023.143
Wishart, D. S. (2007). Improving Early Drug Discovery through ADME Modelling. Drugs in R & D, 8 (6), 349–362. https://doi.org/10.2165/00126839-200708060-00003
Lucas, A. J., Sproston, J. L., Barton, P., Riley, R. J. (2019). Estimating human ADME properties, pharmacokinetic parameters and likely clinical dose in drug discovery. Expert Opinion on Drug Discovery, 14 (12), 1313–1327. https://doi.org/10.1080/17460441.2019.1660642
Siramshetty, V. B., Xu, X., Shah, P. (2023). Artificial Intelligence in ADME Property Prediction. Computational Drug Discovery and Design. New York: Springer, 307–327. https://doi.org/10.1007/978-1-0716-3441-7_17
Wieder, O., Kuenemann, M., Wieder, M., Seidel, T., Meyer, C., Bryant, S. D., Langer, T. (2021). Improved Lipophilicity and Aqueous Solubility Prediction with Composite Graph Neural Networks. Molecules, 26 (20), 6185. https://doi.org/10.3390/molecules26206185
Tosca, E. M., Bartolucci, R., Magni, P. (2021). Application of Artificial Neural Networks to Predict the Intrinsic Solubility of Drug-Like Molecules. Pharmaceutics, 13 (7), 1101. https://doi.org/10.3390/pharmaceutics13071101
Pushkarova, Y., Panchenko, V., Kholin, Y. (2021). Application an Artificial Neural Network for Prediction of Substances Solubility. IEEE EUROCON 2021 – 19th International Conference on Smart Technologies. Lviv: IEEE, 82–87. https://doi.org/10.1109/eurocon52738.2021.9535593
Li, G., Zhang, N., Dai, X., Fan, L. (2024). EnzyACT: A Novel Deep Learning Method to Predict the Impacts of Single and Multiple Mutations on Enzyme Activity. Journal of Chemical Information and Modeling, 64 (15), 5912–5921. https://doi.org/10.1021/acs.jcim.4c00920
Tolle, K. M., Chen, H., Chow, H.-H. (2000). Estimating drug/plasma concentration levels by applying neural networks to pharmacokinetic data sets. Decision Support Systems, 30 (2), 139–151. https://doi.org/10.1016/s0167-9236(00)00094-4
Turner, J. V., Maddalena, D. J., Cutler, D. J. (2004). Pharmacokinetic parameter prediction from drug structure using artificial neural networks. International Journal of Pharmaceutics, 270 (1-2), 209–219. https://doi.org/10.1016/j.ijpharm.2003.10.011
Sutariya, V., Groshev, A., Sadana, P., Bhatia, D., Pathak, Y. (2013). Artificial Neural Network in Drug Delivery and Pharmaceutical Research. The Open Bioinformatics Journal, 7 (1), 49–62. https://doi.org/10.2174/1875036201307010049
Guengerich, F. P. (2011). Mechanisms of Drug Toxicity and Relevance to Pharmaceutical Development. Drug Metabolism and Pharmacokinetics, 26 (1), 3–14. https://doi.org/10.2133/dmpk.dmpk-10-rv-062
Pushkarova, Y., Zaitseva, G., Saker, M. A. (2022). Prediction of Toxicity of Phenols Using Artificial Neural Networks. 2022 12th International Conference on Advanced Computer Information Technologies (ACIT). Ruzomberok: IEEE, 493–496. https://doi.org/10.1109/acit54803.2022.9913174
Pushkarova, Y., Zaitseva, G., Kaliuzhenko, A. (2023). Classification of Residual Solvents by Risk Assessment Using Chemometric Methods. 2023 13th International Conference on Advanced Computer Information Technologies (ACIT). Wrocław: IEEE, 562–565. https://doi.org/10.1109/acit58437.2023.10275405
Hemmerich, J., Ecker, G. F. (2020). In silico toxicology: From structure–activity relationships towards deep learning and adverse outcome pathways. WIREs Computational Molecular Science, 10 (4). https://doi.org/10.1002/wcms.1475
Cavasotto, C. N., Scardino, V. (2022). Machine Learning Toxicity Prediction: Latest Advances by Toxicity End Point. ACS Omega, 7 (51), 47536–47546. https://doi.org/10.1021/acsomega.2c05693
Canzler, S., Schor, J., Busch, W., Schubert, K., Rolle-Kampczyk, U. E., Seitz, H. et al. (2020). Prospects and challenges of multi-omics data integration in toxicology. Archives of Toxicology, 94 (2), 371–388. https://doi.org/10.1007/s00204-020-02656-y
Mukherjee, A., Abraham, S., Singh, A., Balaji, S., Mukunthan, K. S. (2024). From Data to Cure: A Comprehensive Exploration of Multi-omics Data Analysis for Targeted Therapies. Molecular Biotechnology, 67 (4), 1269–1289. https://doi.org/10.1007/s12033-024-01133-6
Li, P., Zhao, L. (2007). Developing early formulations: Practice and perspective. International Journal of Pharmaceutics, 341 (1-2), 1–19. https://doi.org/10.1016/j.ijpharm.2007.05.049
Byron, P. R. (2004). Drug Delivery Devices: Issues in Drug Development. Proceedings of the American Thoracic Society, 1 (4), 321–328. https://doi.org/10.1513/pats.200403-023ms
Takayama, K., Fujikawa, M., Nagai, T. (1999). Artificial Neural Network as a Novel Method to Optimize Pharmaceutical Formulations. Pharmaceutical Research, 16 (1), 1–6. https://doi.org/10.1023/a:1011986823850
Wang, N., Sun, H., Dong, J., Ouyang, D. (2021). PharmDE: A new expert system for drug-excipient compatibility evaluation. International Journal of Pharmaceutics, 607, 120962. https://doi.org/10.1016/j.ijpharm.2021.120962
Soliman, M. E., Adewumi, A. T., Akawa, O. B., Subair, T. I., Okunlola, F. O., Akinsuku, O. E., Khan, S. (2022). Simulation Models for Prediction of Bioavailability of Medicinal Drugs – the Interface Between Experiment and Computation. AAPS PharmSciTech, 23 (3). https://doi.org/10.1208/s12249-022-02229-5
Arav, Y. (2024). Advances in Modeling Approaches for Oral Drug Delivery: Artificial Intelligence, Physiologically Based Pharmacokinetic, and First-Principal Models. https://doi.org/10.20944/preprints202406.0471.v1
Ibrić, S., Jovanović, M., Djurić, Z., Parojčić, J., Solomun, L., Lučić, B. (2007). Generalized regression neural networks in prediction of drug stability. Journal of Pharmacy and Pharmacology, 59 (5), 745–750. https://doi.org/10.1211/jpp.59.5.0017
Wang, S., Di, J., Wang, D., Dai, X., Hua, Y., Gao, X., Zheng, A., Gao, J. (2022). State-of-the-Art Review of Artificial Neural Networks to Predict, Characterize and Optimize Pharmaceutical Formulation. Pharmaceutics, 14 (1), 183. https://doi.org/10.3390/pharmaceutics14010183
Rafienia, M., Amiri, M., Janmaleki, M., Sadeghian, A. (2010). Application of artificial neural networks in controlled drug delivery systems. Applied Artificial Intelligence, 24 (8), 807–820. https://doi.org/10.1080/08839514.2010.508606
Sun, Y., Peng, Y., Chen, Y., Shukla, A. J. (2003). Application of artificial neural networks in the design of controlled release drug delivery systems. Advanced Drug Delivery Reviews, 55 (9), 1201–1215. https://doi.org/10.1016/s0169-409x(03)00119-4
Syrowatka, A., Song, W., Amato, M. G., Foer, D., Edrees, H., Co, Z. et al. (2022). Key use cases for artificial intelligence to reduce the frequency of adverse drug events: a scoping review. The Lancet Digital Health, 4 (2), e137–e148. https://doi.org/10.1016/s2589-7500(21)00229-6
Lee, C. Y., Chen, Y. P. (2021). Descriptive prediction of drug side‐effects using a hybrid deep learning model. International Journal of Intelligent Systems, 36 (6), 2491–2510. https://doi.org/10.1002/int.22389
Schork, N. J. (2019). Artificial Intelligence and Personalized Medicine. Precision Medicine in Cancer Therapy. Cham: Springer, 265–283. https://doi.org/10.1007/978-3-030-16391-4_11
Bonkhoff, A. K., Grefkes, C. (2021). Precision medicine in stroke: towards personalized outcome predictions using artificial intelligence. Brain, 145 (2), 457–475. https://doi.org/10.1093/brain/awab439
Kang, S. (2018). Personalized prediction of drug efficacy for diabetes treatment via patient-level sequential modeling with neural networks. Artificial Intelligence in Medicine, 85, 1–6. https://doi.org/10.1016/j.artmed.2018.02.004
Wanichthanarak, K., Fahrmann, J. F., Grapov, D. (2015). Genomic, Proteomic, and Metabolomic Data Integration Strategies. Biomarker Insights, 10s4, BMI.S29511. https://doi.org/10.4137/bmi.s29511
Valous, N. A., Popp, F., Zörnig, I., Jäger, D., Charoentong, P. (2024). Graph machine learning for integrated multi-omics analysis. British Journal of Cancer, 131 (2), 205–211. https://doi.org/10.1038/s41416-024-02706-7
Alharbi, W. S., Rashid, M. (2022). A review of deep learning applications in human genomics using next-generation sequencing data. Human Genomics, 16 (1). https://doi.org/10.1186/s40246-022-00396-x
van IJzendoorn, D. G. P., Szuhai, K., Briaire-de Bruijn, I. H., Kostine, M., Kuijjer, M. L., Bovée, J. V. M. G. (2019). Machine learning analysis of gene expression data reveals novel diagnostic and prognostic biomarkers and identifies therapeutic targets for soft tissue sarcomas. PLOS Computational Biology, 15 (2), e1006826. https://doi.org/10.1371/journal.pcbi.1006826
Zhang, P., Zhang, W., Sun, W., Xu, J., Hu, H., Wang, L., Wong, L. (2024). Identification of gene biomarkers for brain diseases via multi-network topological semantics extraction and graph convolutional network. BMC Genomics, 25 (1). https://doi.org/10.1186/s12864-024-09967-9
Sundaram, L., Gao, H., Padigepati, S. R., McRae, J. F., Li, Y., Kosmicki, J. A. et al. (2018). Predicting the clinical impact of human mutation with deep neural networks. Nature Genetics, 50 (8), 1161–1170. https://doi.org/10.1038/s41588-018-0167-z
Kellerman, R., Nayshool, O., Barel, O., Paz, S., Amariglio, N., Klang, E., Rechavi, G. (2024). Mutation Pathogenicity Prediction by a Biology Based Explainable AI Multi-Modal Algorithm. https://doi.org/10.1101/2024.06.05.24308476
van Hilten, A., Kushner, S. A., Kayser, M., Ikram, M. A., Adams, H. H. H., Klaver, C. C. W. et al. (2021). GenNet framework: interpretable deep learning for predicting phenotypes from genetic data. Communications Biology, 4 (1). https://doi.org/10.1038/s42003-021-02622-z
Wen, B., Zeng, W., Liao, Y., Shi, Z., Savage, S. R., Jiang, W., Zhang, B. (2020). Deep Learning in Proteomics. Proteomics, 20 (21-22). https://doi.org/10.1002/pmic.201900335
Holley, L. H., Karplus, M. (1989). Protein secondary structure prediction with a neural network. Proceedings of the National Academy of Sciences, 86 (1), 152–156. https://doi.org/10.1073/pnas.86.1.152
Pearce, R., Zhang, Y. (2021). Deep learning techniques have significantly impacted protein structure prediction and protein design. Current Opinion in Structural Biology, 68, 194–207. https://doi.org/10.1016/j.sbi.2021.01.007
Jha, K., Saha, S., Singh, H. (2022). Prediction of protein–protein interaction using graph neural networks. Scientific Reports, 12 (1). https://doi.org/10.1038/s41598-022-12201-9
Li, X., Han, P., Wang, G., Chen, W., Wang, S., Song, T. (2022). SDNN-PPI: self-attention with deep neural network effect on protein-protein interaction prediction. BMC Genomics, 23 (1). https://doi.org/10.1186/s12864-022-08687-2
Huang, Y.-X., Liu, R. (2024). Improved prediction of post-translational modification crosstalk within proteins using DeepPCT. Bioinformatics, 40 (12). https://doi.org/10.1093/bioinformatics/btae675
Gutierrez, C. S., Kassim, A. A., Gutierrez, B. D., Raines, R. T. (2024). Sitetack: A Deep Learning Model that Improves PTM Prediction by Using Known PTMs, 40 (11). https://doi.org/10.1101/2024.06.03.596298
Chiu, Y.-C., Chen, H.-I. H., Zhang, T., Zhang, S., Gorthi, A., Wang, L.-J. et al. (2019). Predicting drug response of tumors from integrated genomic profiles by deep neural networks. BMC Medical Genomics, 12 (S1). https://doi.org/10.1186/s12920-018-0460-9
Smelik, M., Zhao, Y., Li, X., Loscalzo, J., Sysoev, O., Mahmud, F. et al. (2024). An interactive atlas of genomic, proteomic, and metabolomic biomarkers promotes the potential of proteins to predict complex diseases. Scientific Reports, 14 (1). https://doi.org/10.1038/s41598-024-63399-9
Nagy, B., Galata, D. L., Farkas, A., Nagy, Z. K. (2022). Application of Artificial Neural Networks in the Process Analytical Technology of Pharmaceutical Manufacturing – a Review. The AAPS Journal, 24 (4). https://doi.org/10.1208/s12248-022-00706-0
Saha, G. C., Eni, L. N., Saha, H., Parida, P. K., Rathinavelu, R., Jain, S. K., Haldar, B. (2023). Artificial Intelligence in Pharmaceutical Manufacturing: Enhancing Quality Control and Decision Making. Rivista Italiana di Filosofia Analitica Junior, 14 (2), 116–126.
Raisul Islam, M., Zakir Hossain Zamil, M., Eshmam Rayed, M., Mohsin Kabir, M., Mridha, M. F. et al. (2024). Deep Learning and Computer Vision Techniques for Enhanced Quality Control in Manufacturing Processes. IEEE Access, 12, 121449–121479. https://doi.org/10.1109/access.2024.3453664
Zope, K., Singh, K., Nistala, S. H., Basak, A., Rathore, P., Runkana, V. (2019). Anomaly Detection and Diagnosis In Manufacturing Systems: A comparative study of statistical, machine learning and deep learning techniques. Annual Conference of the PHM Society, 11 (1). https://doi.org/10.36001/phmconf.2019.v11i1.815
Motwani, K., Dsouza, R., Dsouza, R., Jose, J. (2022). Counterfeit medicine detection using deep learning. International Journal of Innovative Research in Technology, 9 (3), 118–821.
Alsallal, M., Sharif, M. S., Al-Ghzawi, B., Mlkat al Mutoki, S. M. (2018). A Machine Learning Technique to Detect Counterfeit Medicine Based on X-Ray Fluorescence Analyser. 2018 International Conference on Computing, Electronics & Communications Engineering (ICCECE). Southend: IEEE, 118–122. https://doi.org/10.1109/iccecome.2018.8659110
Liu, X., Meehan, J., Tong, W., Wu, L., Xu, X., Xu, J. (2020). DLI-IT: a deep learning approach to drug label identification through image and text embedding. BMC Medical Informatics and Decision Making, 20 (1). https://doi.org/10.1186/s12911-020-1078-3
Rimdusit, P., Aphiromyarnont, P., Phimoltares, S., Panthuwadeethorn, S. (2024). Extracting Information from Drug Label Using Image and Text Processing. 2024 21st International Joint Conference on Computer Science and Software Engineering (JCSSE). Phuket: IEEE, 377–382. https://doi.org/10.1109/jcsse61278.2024.10613666
Choi, S. H., Poon, C. H. (2008). An RFID-based anti-counterfeiting system. IAENG International Journal of Computer Science, 35 (1).
Rahman, F., Ahamed, S. I. (2012). Efficient detection of counterfeit products in large-scale RFID systems using batch authentication protocols. Personal and Ubiquitous Computing, 18 (1), 177–188. https://doi.org/10.1007/s00779-012-0629-8
Bhardwaj, H., Khute, S., Sahu, R. K., Jangde, R. K. (2025). Recent Advances in Water-Soluble Polymer and Polymeric Nanoparticles for Pharmaceutical Application. Current Nanoscience, 21 (4), 565–584. https://doi.org/10.2174/0115734137294889240314032718
Hariyadi, D. M., Islam, N. (2020). Current Status of Alginate in Drug Delivery. Advances in Pharmacological and Pharmaceutical Sciences, 2020, 1–16. https://doi.org/10.1155/2020/8886095
Kadajji, V. G., Betageri, G. V. (2011). Water Soluble Polymers for Pharmaceutical Applications. Polymers, 3 (4), 1972–2009. https://doi.org/10.3390/polym3041972
Al-Roujayee, A. S., Hilaj, E., Deepak, A., Jyothi, S. R., Hamid, J. A., Ariffin, I. A. et al. (2024). Alginate-based systems: advancements in drug delivery and wound healing. International Journal of Polymeric Materials and Polymeric Biomaterials, 74 (9), 846–874. https://doi.org/10.1080/00914037.2024.2375343
Sharma, A., Gupta, M. N. (2002). Three phase partitioning of carbohydrate polymers: separation and purification of alginates. Carbohydrate Polymers, 48 (4), 391–395. https://doi.org/10.1016/s0144-8617(01)00313-7
Yang, J.-S., Xie, Y.-J., He, W. (2011). Research progress on chemical modification of alginate: A review. Carbohydrate Polymers, 84 (1), 33–39. https://doi.org/10.1016/j.carbpol.2010.11.048
Rhein-Knudsen, N., Ale, M. T., Ajalloueian, F., Meyer, A. S. (2017). Characterization of alginates from Ghanaian brown seaweeds: Sargassum spp. and Padina spp. Food Hydrocolloids, 71, 236–244. https://doi.org/10.1016/j.foodhyd.2017.05.016
Otterlei, M., Østgaard, K., Skjåk-Bræk, G., Smidsrød, O., Soon-Shiong, P., Espevik, T. (1991). Induction of Cytokine Production from Human Monocytes Stimulated with Alginate. Journal of Immunotherapy, 10 (4), 286–291. https://doi.org/10.1097/00002371-199108000-00007
Johnson, F. A., Craig, D. Q. M., Mercer, A. D. (1997). Characterization of the Block Structure and Molecular Weight of Sodium Alginates. Journal of Pharmacy and Pharmacology, 49 (7), 639–643. https://doi.org/10.1111/j.2042-7158.1997.tb06085.x
Fu, S., Thacker, A., Sperger, D. M., Boni, R. L., Buckner, I. S., Velankar, S. et al. (2011). Relevance of Rheological Properties of Sodium Alginate in Solution to Calcium Alginate Gel Properties. AAPS PharmSciTech, 12 (2), 453–460. https://doi.org/10.1208/s12249-011-9587-0
Ching, S. H., Bansal, N., Bhandari, B. (2015). Alginate gel particles–A review of production techniques and physical properties. Critical Reviews in Food Science and Nutrition, 57 (6), 1133–1152. https://doi.org/10.1080/10408398.2014.965773
Szekalska, M., Puciłowska, A., Szymańska, E., Ciosek, P., Winnicka, K. (2016). Alginate: Current Use and Future Perspectives in Pharmaceutical and Biomedical Applications. International Journal of Polymer Science, 2016, 1–17. https://doi.org/10.1155/2016/7697031
Deng, Y., Yang, N., Valentine Okoro, O., Shavandi, A., Nie, L. (2022). Alginate-Based Composite and Its Biomedical Applications. Properties and Applications of Alginates. https://doi.org/10.5772/intechopen.99494
Tam, S. K., Dusseault, J., Bilodeau, S., Langlois, G., Hallé, J., Yahia, L. (2011). Factors influencing alginate gel biocompatibility. Journal of Biomedical Materials Research Part A, 98A (1), 40–52. https://doi.org/10.1002/jbm.a.33047
de Vos, P., Faas, M. M., Strand, B., Calafiore, R. (2006). Alginate-based microcapsules for immunoisolation of pancreatic islets. Biomaterials, 27 (32), 5603–5617. https://doi.org/10.1016/j.biomaterials.2006.07.010
Kesharwani, P., Sharma, S., Chaudhary, V., Goyal, R., Gautam, R. K. (2024). Biomedical Applications of Alginates. Biopolymers for Biomedical Applications, 53–86. https://doi.org/10.1002/9781119865452.ch3
Ahmad Raus, R., Wan Nawawi, W. M. F., Nasaruddin, R. R. (2021). Alginate and alginate composites for biomedical applications. Asian Journal of Pharmaceutical Sciences, 16 (3), 280–306. https://doi.org/10.1016/j.ajps.2020.10.001
Venkatesan, J., Bhatnagar, I., Manivasagan, P., Kang, K.-H., Kim, S.-K. (2015). Alginate composites for bone tissue engineering: A review. International Journal of Biological Macromolecules, 72, 269–281. https://doi.org/10.1016/j.ijbiomac.2014.07.008
Bidarra, S. J., Barrias, C. C., Granja, P. L. (2014). Injectable alginate hydrogels for cell delivery in tissue engineering. Acta Biomaterialia, 10 (4), 1646–1662. https://doi.org/10.1016/j.actbio.2013.12.006
Grothe, T., Grimmelsmann, N., Homburg, S. V., Ehrmann, A. (2017). Possible applications of nano-spun fabrics and materials. Materials Today: Proceedings, 4, S154–S159. https://doi.org/10.1016/j.matpr.2017.09.180
Namviriyachote, N., Lipipun, V., Akkhawattanangkul, Y., Charoonrut, P., Ritthidej, G. C. (2019). Development of polyurethane foam dressing containing silver and asiaticoside for healing of dermal wound. Asian Journal of Pharmaceutical Sciences, 14 (1), 63–77. https://doi.org/10.1016/j.ajps.2018.09.001
Holak, P., Adamiak, Z., Babinska, I., Jalynski, M., Jastrzebski, P., Grabarczyk, L. et al. (2019). The Influence of Haemostatic Dressing Prototypes for the Emergency Services on the Histopathological Parameters of Porcine Muscle. In Vivo, 33 (3), 723–729. https://doi.org/10.21873/invivo.11531
Kurczewska, J., Pecyna, P., Ratajczak, M., Gajęcka, M., Schroeder, G. (2017). Halloysite nanotubes as carriers of vancomycin in alginate-based wound dressing. Saudi Pharmaceutical Journal, 25 (6), 911–920. https://doi.org/10.1016/j.jsps.2017.02.007
Sharifi, F., Sooriyarachchi, A. C., Altural, H., Montazami, R., Rylander, M. N., Hashemi, N. (2016). Fiber Based Approaches as Medicine Delivery Systems. ACS Biomaterials Science & Engineering, 2 (9), 1411–1431. https://doi.org/10.1021/acsbiomaterials.6b00281
Lai, W.-F., Rogach, A. L. (2017). Hydrogel-Based Materials for Delivery of Herbal Medicines. ACS Applied Materials & Interfaces, 9 (13), 11309–11320. https://doi.org/10.1021/acsami.6b16120
Xue, W., Zhang, M., Zhao, F., Wang, F., Gao, J., Wang, L. (2019). Long-term durability antibacterial microcapsules with plant-derived Chinese nutgall and their applications in wound dressing. E-Polymers, 19 (1), 268–276. https://doi.org/10.1515/epoly-2019-0027
Lin, N., Gèze, A., Wouessidjewe, D., Huang, J., Dufresne, A. (2016). Biocompatible Double-Membrane Hydrogels from Cationic Cellulose Nanocrystals and Anionic Alginate as Complexing Drugs Codelivery. ACS Applied Materials & Interfaces, 8 (11), 6880–6889. https://doi.org/10.1021/acsami.6b00555
Deng, X., Sang, S., Li, P., Li, G., Gao, F., Sun, Y., Zhang, W., Hu, J. (2013). Preparation, Characterization, and Mechanistic Understanding of Pd‐Decorated ZnO Nanowires for Ethanol Sensing. Journal of Nanomaterials, 2013 (1). https://doi.org/10.1155/2013/297676
Buryy, O., Ubizskii, S. В. (2017). Nanostructured gas sensors: the state of the art and perspectives for research. Visnyk Natsionalnoho universytetu «Lvivska politekhnika». Seriya: Radioelektronika ta telekomunikatsiyi, 885, 113–131. Available at: https://science.lpnu.ua/sites/default/files/journal-paper/2018/jun/13517/17.pdf
Popov, O., Ivaschenko, T., Markina, L., Yatsyshyn, T., Iatsyshyn, A., Lytvynenko, O.; Zaporozhets, A. (Ed.) (2023). Peculiarities of Specialized Software Tools Used for Consequences Assessment of Accidents at Chemically Hazardous Facilities. Systems, Decision and Control in Energy V. Springer, 779–798. https://doi.org/10.1007/978-3-031-35088-7_45
Parihar, V., Raja, M., Paulose, R. (2018). A Brief Review of Structural, Electrical and Electrochemical Properties of Zinc Oxide Nanoparticles. Reviews on advanced materials science, 53 (2), 119–130. https://doi.org/10.1515/rams-2018-0009
Deyneko, N., Kovalev, P., Semkiv, O., Khmyrov, I., Shevchenko, R. (2019). Development of a technique for restoring the efficiency of film ITO/CdS/CdTe/Cu/Au SCs after degradation. Eastern-European Journal of Enterprise Technologies, 1 (5 (97)), 6–12. https://doi.org/10.15587/1729-4061.2019.156565
Deyneko, N., Kryvulkin, I., Matiushenko, M., Tarasenko, O., Khmyrov, I., Khmyrova, A., Shevchenko, R. (2019). Investigation of photoelectric converters with a base cadmium telluride layer with a decrease in its thickness for tandem and two-sided sensitive instrument structures. EUREKA: Physics and Engineering, 5, 73–80. https://doi.org/10.21303/2461-4262.2019.001002
Ghavam, S., Vahdati, M., Wilson, I. A. G., Styring, P. (2021). Sustainable Ammonia Production Processes. Frontiers in Energy Research, 9. https://doi.org/10.3389/fenrg.2021.580808
Kwak, D., Lei, Y., Maric, R. (2019). Ammonia gas sensors: A comprehensive review. Talanta, 204, 713–730. https://doi.org/10.1016/j.talanta.2019.06.034
Arnone, M., Koppisch, D., Smola, T., Gabriel, S., Verbist, K., Visser, R. (2015). Hazard banding in compliance with the new Globally Harmonised System (GHS) for use in control banding tools. Regulatory Toxicology and Pharmacology, 73 (1), 287–295. https://doi.org/10.1016/j.yrtph.2015.07.014
Corkery, G., Ward, S., Kenny, C., Hemmingway, P. (2013). Monitoring environmental parameters in poultry production facilities. Computer Aided Process Engineering, CAPE Forum 2013. Graz: Graz University of Technology. Available at: http://hdl.handle.net/10197/4257
Popov, O., Kovach, V., Iatsyshyn, A., Pecheny, V., Kutsenko, V., Lahoiko, A. Modern Measuring Tools to Develop Efficient Atmospheric Air Monitoring Systems Based on UAVs. Systems, Decision and Control in Energy VI: Volume II: Power Engineering and Environmental Safety. Cham: Springer Nature Switzerland, 2024. 519-535.
Duong, P. A., Bae, J.-W., Lee, C., Yang, D. H., Kang, H. (2025). Numerical Study on Ammonia Dispersion and Explosion Characteristics in Confined Space of Marine Fuel Preparation Room. Journal of Marine Science and Engineering, 13 (7), 1235. https://doi.org/10.3390/jmse13071235
Vambol, S., Vambol, V., Kondratenko, O., Suchikova, Y., Hurenko, O. (2017). Assessment of improvement of ecological safety of power plants by arranging the system of pollutant neutralization. Eastern-European Journal of Enterprise Technologies, 3 (10 (87)), 63–73. https://doi.org/10.15587/1729-4061.2017.102314
Tu, Y., Kyle, C., Luo, H., Zhang, D.-W., Das, A., Briscoe, J. et al. (2020). Ammonia Gas Sensor Response of a Vertical Zinc Oxide Nanorod-Gold Junction Diode at Room Temperature. ACS Sensors, 5 (11), 3568–3575. https://doi.org/10.1021/acssensors.0c01769
Seekaew, Y., Pon-On, W., Wongchoosuk, C. (2019). Ultrahigh Selective Room-Temperature Ammonia Gas Sensor Based on Tin–Titanium Dioxide/reduced Graphene/Carbon Nanotube Nanocomposites by the Solvothermal Method. ACS Omega, 4 (16), 16916–16924. https://doi.org/10.1021/acsomega.9b02185
Maity, A., Raychaudhuri, A. K., Ghosh, B. (2019). High sensitivity NH3 gas sensor with electrical readout made on paper with perovskite halide as sensor material. Scientific Reports, 9 (1). https://doi.org/10.1038/s41598-019-43961-6
Husain, A. (2021). Electrical conductivity based ammonia, methanol and acetone vapour sensing studies on newly synthesized polythiophene/molybdenum oxide nanocomposite. Journal of Science: Advanced Materials and Devices, 6 (4), 528–537. https://doi.org/10.1016/j.jsamd.2021.07.002
Seekaew, Y., Lokavee, S., Phokharatkul, D., Wisitsoraat, A., Kerdcharoen, T., Wongchoosuk, C. (2014). Low-cost and flexible printed graphene – PEDOT:PSS gas sensor for ammonia detection. Organic Electronics, 15 (11), 2971–2981. https://doi.org/10.1016/j.orgel.2014.08.044
Kumar, L., Rawal, I., Kaur, A., Annapoorni, S. (2017). Flexible room temperature ammonia sensor based on polyaniline. Sensors and Actuators B: Chemical, 240, 408–416. https://doi.org/10.1016/j.snb.2016.08.173
Zhao, S., Shen, Y., Yan, X., Zhou, P., Yin, Y., Lu, R. et al. (2019). Complex-surfactant-assisted hydrothermal synthesis of one-dimensional ZnO nanorods for high-performance ethanol gas sensor. Sensors and Actuators B: Chemical, 286, 501–511. https://doi.org/10.1016/j.snb.2019.01.127
Xuan, J., Zhao, G., Sun, M., Jia, F., Wang, X., Zhou, T. et al. (2020). Low-temperature operating ZnO-based NO2 sensors: a review. RSC Advances, 10 (65), 39786–39807. https://doi.org/10.1039/d0ra07328h
Liu, W., Chen, Z., Si, X., Tong, H., Guo, J., Zhang, Z. et al. (2023). Facile synthesis of Pt catalysts functionalized porous ZnO nanowires with enhanced gas-sensing properties. Journal of Alloys and Compounds, 947, 169486. https://doi.org/10.1016/j.jallcom.2023.169486
Miasoiedova, A., Minska, N., Shevchenko, R., Azarenkо, O., Lukashenko, V., Kyrychenko, O. et al. (2023). Improving the manufacturing technology of sensing gas sensors based on zinc oxide by using the method of magnetron sputtering on direct current. Eastern-European Journal of Enterprise Technologies, 2 (5 (122)), 31–37. https://doi.org/10.15587/1729-4061.2023.277428
Minska, N., Hvozd, V., Shevchenko, O., Slepuzhnikov, Y., Murasov, R., Khrystych, V. et al. (2023). Devising technological solutions for gas sensors based on zinc oxide for use at critical infrastructure facilities. Eastern-European Journal of Enterprise Technologies, 4 (5 (124)), 34–40. https://doi.org/10.15587/1729-4061.2023.286546
Agarwal, S., Rai, P., Gatell, E. N., Llobet, E., Güell, F., Kumar, M., Awasthi, K. (2019). Gas sensing properties of ZnO nanostructures (flowers/rods) synthesized by hydrothermal method. Sensors and Actuators B: Chemical, 292, 24–31. https://doi.org/10.1016/j.snb.2019.04.083
Dral, A. P., ten Elshof, J. E. (2018). 2D metal oxide nanoflakes for sensing applications: Review and perspective. Sensors and Actuators B: Chemical, 272, 369–392. https://doi.org/10.1016/j.snb.2018.05.157
Bian, H., Ma, S., Sun, A., Xu, X., Yang, G., Yan, S. et al. (2016). Improvement of acetone gas sensing performance of ZnO nanoparticles. Journal of Alloys and Compounds, 658, 629–635. https://doi.org/10.1016/j.jallcom.2015.09.217
Yan, H., Song, P., Zhang, S., Yang, Z., Wang, Q. (2016). Facile synthesis, characterization and gas sensing performance of ZnO nanoparticles-coated MoS2 nanosheets. Journal of Alloys and Compounds, 662, 118–125. https://doi.org/10.1016/j.jallcom.2015.12.066
Umar, A., Khan, M. A., Kumar, R., Algarni, H. (2018). Ag-Doped ZnO Nanoparticles for Enhanced Ethanol Gas Sensing Application. Journal of Nanoscience and Nanotechnology, 18 (5), 3557–3562. https://doi.org/10.1166/jnn.2018.14651
Zhang, D., Yang, Z., Li, P., Zhou, X. (2019). Ozone gas sensing properties of metal-organic frameworks-derived In2O3 hollow microtubes decorated with ZnO nanoparticles. Sensors and Actuators B: Chemical, 301, 127081. https://doi.org/10.1016/j.snb.2019.127081
Hübner, M., Simion, C. E., Tomescu-Stănoiu, A., Pokhrel, S., Bârsan, N., Weimar, U. (2011). Influence of humidity on CO sensing with p-type CuO thick film gas sensors. Sensors and Actuators B: Chemical, 153 (2), 347–353. https://doi.org/10.1016/j.snb.2010.10.046

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