ADVANCED FABRICATION AND CHARACTERIZATION OF SILVER NANOPARTICLES USING AI TECHNIQUES
DOI:
https://doi.org/10.22159/ijap.2025v17i5.55011Keywords:
Silver nanoparticles, Machine learning, Green synthesis, Toxicity prediction, Artificial neural networks, Support vector machines, Functionalization, Nanomedicine, BiosensorsAbstract
The integration of machine learning (ML) into nanoscience has transformed the fabrication and characterization of silver nanoparticles (AgNPs), enabling precise control over particle size, shape, and functionalization. This review highlights the application of supervised and unsupervised ML models, such as artificial neural networks (ANNs), support vector machines (SVMs), and decision trees, in optimizing AgNP synthesis parameters, including temperature, pH, and reducing agent concentration. Emphasis is placed on green synthesis methods using plant extracts, where ML predicts eco-friendly conditions with minimal experimental input. Characterization techniques benefit from ML-driven image and spectral data analysis, enhancing speed and accuracy. ML is also pivotal in predicting the toxicity and biocompatibility of AgNPs, reducing reliance on animal testing and enabling safer biomedical applications. ML reduced synthesis optimization time by 30%," and to specify the types of ML techniques applied, like neural networks or support vector machines (SVMs). Furthermore, ML enhances functionalization strategies for drug delivery, biosensing, and environmental remediation. By quantifying performance outcomes and improving reproducibility, ML supports the scalable and sustainable development of AgNPs. This review offers a detailed synthesis of current advances and identifies future opportunities for intelligent, data-driven nanomaterial design.
References
1. Zhang XF, Liu ZG, Shen W, Gurunathan S. Silver nanoparticles: synthesis, characterization properties, applications and therapeutic approaches. Int J Mol Sci. 2016;17(9):1534. doi: 10.3390/ijms17091534, PMID 27649147.
2. Kalantari K, Mostafavi E, Afifi AM, Izadiyan Z, Jahangirian H, Rafiee Moghaddam R. Wound dressings functionalized with silver nanoparticles: promises and pitfalls. Nanoscale. 2020;12(4):2268-91. doi: 10.1039/c9nr08234d, PMID 31942896.
3. Eby DM, Luckarift HR, Johnson GR. Hybrid antimicrobial enzyme and silver nanoparticle coatings for medical instruments. ACS Appl Mater Interfaces. 2009;1(7):1553-60. doi: 10.1021/am9002155, PMID 20355960.
4. Matalqah S, Lafi Z, Asha SY. Hyaluronic acid in nanopharmaceuticals: an overview. Curr Issues Mol Biol. 2024;46(9):10444-61. doi: 10.3390/cimb46090621, PMID 39329973.
5. Jain J, Arora S, Rajwade JM, Omray P, Khandelwal S, Paknikar KM. Silver nanoparticles in therapeutics: development of an antimicrobial gel formulation for topical use. Mol Pharm. 2009;6(5):1388-401. doi: 10.1021/mp900056g, PMID 19473014.
6. Otunola GA, Afolayan AJ. In vitro antibacterial, antioxidant and toxicity profile of silver nanoparticles green synthesized and characterized from aqueous extract of a spice blend formulation. Biotechnol Biotechnol Equip. 2018;32(3):724-33. doi: 10.1080/13102818.2018.1448301.
7. Badnore AU, Sorde KI, Datir KA, Ananthanarayan L, Pratap AP, Pandit AB. Preparation of antibacterial peel-off facial mask formulation incorporating biosynthesized silver nanoparticles. Appl Nanosci. 2019;9(2):279-87. doi: 10.1007/s13204-018-0934-2.
8. Zhao Y, Zeng Q, Wu F, Li J, Pan Z, Shen P. Novel naproxen peptide conjugated amphiphilic dendrimer self-assembly micelles for targeting drug delivery to osteosarcoma cells. RSC Adv. 2016;6(65):60327-35. doi: 10.1039/C6RA15022E.
9. Ong C, Lim JZ, Ng CT, Li JJ, Yung LY, Bay BH. Silver nanoparticles in cancer: therapeutic efficacy and toxicity. Curr Med Chem. 2013;20(6):772-81, PMID 23298139.
10. Prasher P, Sharma M, Mudila H, Gupta G, Sharma AK, Kumar D. Emerging trends in clinical implications of bio-conjugated silver nanoparticles in drug delivery. Colloid and Interface Science Communications. 2020;35:100244. doi: 10.1016/j.colcom.2020.100244.
11. Mandal AK, JO N. Silver nanoparticles as drug delivery vehicle against infections. Glob J Nanomed. 2017;3(2):1-4. doi: 10.19080/GJN.2017.03.555607.
12. Patra S, Mukherjee S, Barui AK, Ganguly A, Sreedhar B, Patra CR. Green synthesis characterization of gold and silver nanoparticles and their potential application for cancer therapeutics. Mater Sci Eng C Mater Biol Appl. 2015;53:298-309. doi: 10.1016/j.msec.2015.04.048, PMID 26042718.
13. Mo L, Guo Z, Yang L, Zhang Q, Fang Y, Xin Z. Silver nanoparticles based ink with moderate sintering in flexible and printed electronics. Int J Mol Sci. 2019;20(9):2124. doi: 10.3390/ijms20092124, PMID 31036787.
14. Sudarman F, Shiddiq M, Armynah B, Tahir DJ. Silver nanoparticles (AgNPs) synthesis methods as heavy-metal sensors: a review. International Journal of Environmental Science and Technology. 2023;20(8):9351-68. doi: 10.1007/s13762-022-04745-0.
15. Khalil AM, Hassan ML, Ward AA. Novel nanofibrillated cellulose/polyvinylpyrrolidone/silver nanoparticles films with electrical conductivity properties. Carbohydr Polym. 2017;157:503-11. doi: 10.1016/j.carbpol.2016.10.008, PMID 27987955.
16. Yu Y, Zhou Z, Huang G, Cheng H, Han L, Zhao S. Purifying water with silver nanoparticles (AgNPs)-incorporated membranes: recent advancements and critical challenges. Water Res. 2022;222:118901. doi: 10.1016/j.watres.2022.118901, PMID 35933814.
17. Zahoor M, Nazir N, Iftikhar M, Naz S, Zekker I, Burlakovs J. A review on silver nanoparticles: classification various methods of synthesis and their potential roles in biomedical applications and water treatment. Water. 2021;13(16):2216. doi: 10.3390/w13162216.
18. Rani P, Kumar V, Singh PP, Matharu AS, Zhang W, Kim KH. Highly stable AgNPs prepared via a novel green approach for catalytic and photocatalytic removal of biological and non-biological pollutants. Environ Int. 2020;143:105924. doi: 10.1016/j.envint.2020.105924, PMID 32659527.
19. Greener JG, Kandathil SM, Moffat L, Jones DT. A guide to machine learning for biologists. Nat Rev Mol Cell Biol. 2022;23(1):40-55. doi: 10.1038/s41580-021-00407-0, PMID 34518686.
20. Karniadakis GE, Kevrekidis IG, Lu L, Perdikaris P, Wang S, Yang LJ. Physics-informed machine learning. Nat Rev Phys. 2021;3(6):422-40. doi: 10.1038/s42254-021-00314-5.
21. He L, Bai L, Dionysiou DD, Wei Z, Spinney R, Chu C. Applications of computational chemistry, artificial intelligence and machine learning in aquatic chemistry research. Chemical Engineering Journal. 2021;426:131810. doi: 10.1016/j.cej.2021.131810.
22. Fantin Irudaya Raj E, Balaji M. Application of deep learning and machine learning in pattern recognition. Advance concepts of image processing and pattern recognition: an effective solution for global challenges: springer; 2022. p. 63-89.
23. Paullada A, Raji ID, Bender EM, Denton E, Hanna AJ. Data and its (dis)contents: a survey of dataset development and use in machine learning research. Patterns (N Y). 2021;2(11):100336. doi: 10.1016/j.patter.2021.100336, PMID 34820643.
24. Libbrecht MW, Noble WS. Machine learning applications in genetics and genomics. Nat Rev Genet. 2015;16(6):321-32. doi: 10.1038/nrg3920, PMID 25948244.
25. Baqui PO, Marra V, Casarini L, Angulo R, Diaz Garcia LA, Hernandez Monteagudo C. The miniJPAS survey: star galaxy classification using machine learning. A&A. 2021;645:A87. doi: 10.1051/0004-6361/202038986.
26. Artrith N, Butler KT, Coudert FX, Han S, Isayev O, Jain A. Best practices in machine learning for chemistry. Nat Chem. 2021;13(6):505-8. doi: 10.1038/s41557-021-00716-z, PMID 34059804.
27. Braca P, Millefiori LM, Aubry A, Marano S, De Maio A, Willett PJ. Statistical hypothesis testing based on machine learning: large deviations analysis. IEEE Open Journal of Signal Processing. 2022;3:464-95.
28. Sarker IH. Machine learning: algorithms real world applications and research directions. SN Comput Sci. 2021;2(3):160. doi: 10.1007/s42979-021-00592-x.
29. Mekki Berrada F, Ren Z, Huang T, Wong WK, Zheng F, Xie J. Two-step machine learning enables optimized nanoparticle synthesis. NPJ Comput Mater. 2021;7(1):55. doi: 10.1038/s41524-021-00520-w.
30. Dong B, Xue N, Mu G, Wang M, Xiao Z, Dai L. Synthesis of monodisperse spherical AgNPs by ultrasound intensified lee-meisel method and quick evaluation via machine learning. Ultrason Sonochem. 2021;73:105485. doi: 10.1016/j.ultsonch.2021.105485, PMID 33588207.
31. Mathumathi M, Vetriselvi T, Matheswaran P, Nandhini R, Selvi A. Silver nano-particles size prediction using deep learning model with green method. Mater Today Proc. 2022;69:1193-9. doi: 10.1016/j.matpr.2022.08.253.
32. Kashiwagi T, Sue K, Takebayashi Y, Ono T. High-throughput synthesis of silver nanoplates and optimization of optical properties by machine learning. Chem Eng Sci. 2022;262:118009. doi: 10.1016/j.ces.2022.118009.
33. Nathanael K, Cheng S, Kovalchuk NM, Arcucci R, Simmons MJ. Optimization of microfluidic synthesis of silver nanoparticles: a generic approach using machine learning. Chem Eng Res Des. 2023;193:65-74. doi: 10.1016/j.cherd.2023.03.007.
34. Zhang Z, Lin J, Chen Z. Predicting the effect of silver nanoparticles on soil enzyme activity using the machine learning method: type size dose and exposure time. J Hazard Mater. 2023;457:131789. doi: 10.1016/j.jhazmat.2023.131789, PMID 37301072.
35. Prasad A, Santra TS, Jayaganthan R. A study on prediction of size and morphology of ag nanoparticles using machine learning models for biomedical applications. Metals. 2024;14(5):539. doi: 10.3390/met14050539.
36. Kourou K, Exarchos TP, Exarchos KP, Karamouzis MV, Fotiadis DI, Journal SB. Machine learning applications in cancer prognosis and prediction. Computational and Structural Biotechnology Journal. doi: 10.1016/j.csbj.2014.11.0052015;13:8-17.
37. Vu MT, Adalı T, Ba D, Buzsaki G, Carlson D, Heller K. A shared vision for machine learning in neuroscience. J Neurosci. 2018;38(7):1601-7. doi: 10.1523/JNEUROSCI.0508-17.2018, PMID 29374138.
38. Xie Y, Sattari K, Zhang C, Lin J. Toward autonomous laboratories: convergence of artificial intelligence and experimental automation. Progress in Materials Science. 2023 Feb;132:101043. doi: 10.1016/j.pmatsci.2022.101043.
39. Siau K, Wang W. Building trust in artificial intelligence machine learning and robotics. Cutter Business Technology Journal. 2018;31(2):47-53.
40. Peng J, Han H, Yi Y, Huang H, Xie LJ. Machine learning and deep learning modeling and simulation for predicting. Chemosphere. 2022 Dec;308(Pt 1):136353. doi: 10.1016/j.chemosphere.2022.136353.
41. Pineau J, Vincent Lamarre P, Sinha K, Lariviere V, Beygelzimer A, D Alche Buc F. Improving reproducibility in machine learning research (a report from the neurips 2019 reproducibility program). Journal of Machine Learning Research. 2021;22(164):1-20.
42. Huang G, Guo Y, Chen Y, Nie Z. Application of machine learning in material synthesis and property prediction. Materials (Basel). 2023;16(17):5977. doi: 10.3390/ma16175977, PMID 37687675, PMCID PMC10488794.
43. Mary P, Mujeeb A. A machine learning framework for the prediction of antibacterial capacity of silver nanoparticles. Nano Express. 2024;5(2):025022. doi: 10.1088/2632-959X/ad4c80.
44. Findlay MR, Freitas DN, Mobed Miremadi M, Wheeler KE. Machine learning provides predictive analysis into silver nanoparticle protein corona formation from physicochemical properties. Environ Sci Nano. 2018;5(1):64-71. doi: 10.1039/C7EN00466D, PMID 29881624.
45. Rufina R, DJ, Uthayakumar H, Thangavelu P. Prediction of the size of green synthesized silver nanoparticles using RSM-ANN-LM hybrid modeling approach. Chem Phys Impact. 2023;6:100231. doi: 10.1016/j.chphi.2023.100231.
46. Furxhi I, Faccani L, Zanoni I, Brigliadori A, Vespignani M, Costa AL. Design rules applied to silver nanoparticles synthesis: a practical example of machine learning application. Comput Struct Biotechnol J. 2024;25:20-33. doi: 10.1016/j.csbj.2024.02.010, PMID 38444982.
47. Uthayakumar H, Thangavelu P. Prediction of the size of green synthesized silver nanoparticles using RSM-ANN-LM hybrid modeling approach. Chemical Physics Impact. 2023 Jun;6:100231. doi: 10.1016/j.chphi.2023.100231.
48. Alshaer W, Nsairat H, Lafi Z, Hourani OM, Al Kadash A, Esawi E. Quality by design approach in liposomal formulations: robust product development. Molecules. 2022;28(1):10. doi: 10.3390/molecules28010010, PMID 36615205.
49. Nunez RN, Veglia AV, Pacioni NL. Improving reproducibility between batches of silver nanoparticles using an experimental design approach. Microchemical Journal. 2018;141:110-7. doi: 10.1016/j.microc.2018.05.017.
50. Saadat A, Dehghani Varniab A, Madani SM. Prediction of the antibacterial activity of the green synthesized silver nanoparticles against gram negative and positive bacteria by using machine learning algorithms. J Nanomater. 2022;2022(1):4986826. doi: 10.1155/2022/4986826.
51. Mungwari CP, King'ondu CK, Sigauke P, Obadele BA. Conventional and modern techniques for bioactive compounds recovery from plants. Sci Afr. 2025;27:e02509. doi: 10.1016/j.sciaf.2024.
52. Lee B, Yoon S, Lee JW, Kim Y, Chang J, Yun J. Statistical characterization of the morphologies of nanoparticles through machine learning based electron microscopy image analysis. ACS Nano. 2020;14(12):17125-33. doi: 10.1021/acsnano.0c06809, PMID 33231065.
53. Klinavicius T, Khinevich N, Tamuleviciene A, Vidal L, Tamulevicius S, Tamulevicius T. Deep learning methods for colloidal silver nanoparticle concentration and size distribution determination from UV–vis extinction spectra. J Phys Chem C. 2024;128(23):9662-75. doi: 10.1021/acs.jpcc.4c02459.
54. Sahin F, Celik N, Camdal A, Sakir M, Ceylan A, Ruzi M. Machine learning assisted pesticide detection on a flexible surface enhanced raman scattering substrate prepared by silver nanoparticles. ACS Appl Nano Mater. 2022;5(9):13112-22. doi: 10.1021/acsanm.2c02897.
55. Hassan SA, Ghadam P, editors. Random forest model resolves the challenges against multi-drug resistant bacteria by AgNPs. Chem Process. 2023;9:2-4. doi: 10.3390/ecsoc-27-16150.
56. Khan MS, Sidek LM, Kumar P, Alkhadher SA, Basri H, Zawawi MH. Machine learning based model to predict catalytic performance on removal of hazardous nitrophenols and azo dyes pollutants from wastewater. Int J Biol Macromol. 2024;278(3):134701. doi: 10.1016/j.ijbiomac.2024.134701, PMID 39151852.
57. Ibrahim S, Ahmad Z, Manzoor MZ, Mujahid M, Faheem Z, Adnan AJ. Optimization for biogenic microbial synthesis of silver nanoparticles through response surface methodology, characterization their antimicrobial antioxidant and catalytic potential. Sci Rep. 2021;11(1):770. doi: 10.1038/s41598-020-80805-0, PMID 33436966.
58. Ramalingam M, Jaisankar A, Cheng L, Krishnan S, Lan L, Hassan A. Impact of nanotechnology on conventional and artificial intelligence based biosensing strategies for the detection of viruses. Discov Nano. 2023;18(1):58. doi: 10.1186/s11671-023-03842-4, PMID 37032711.
59. Jamkhande PG, Ghule NW, Bamer AH, Kalaskar MG. Metal nanoparticles synthesis: an overview on methods of preparation advantages and disadvantages and applications. J Drug Deliv Sci Technol. 2019;53:101174. doi: 10.1016/j.jddst.2019.101174.
60. Karimadom BR, Kornweitz H. Mechanism of producing metallic nanoparticles with an emphasis on silver and gold nanoparticles using bottom-up methods. Molecules. 2021;26(10):2968. doi: 10.3390/molecules26102968, PMID 34067624.
61. Sarkar A, Kapoor S, Mukherjee T. Synthesis and characterisation of silver nanoparticles in viscous solvents and its transfer into non-polar solvents. Res Chem Intermed. 2010;36(4):411-21. doi: 10.1007/s11164-010-0151-4.
62. Lafi Z, Alshaer W, Hatmal MM, Zihlif M, Alqudah DA, Nsairat H. Aptamer-functionalized pH-sensitive liposomes for a selective delivery of echinomycin into cancer cells. RSC Adv. 2021;11(47):29164-77. doi: 10.1039/d1ra05138e, PMID 35479561, PMCID PMC9040599.
63. Al Azzawi H, Alshaer W, Esawi E, Lafi Z, Abuarqoub D, Zaza R. Multifunctional nanoparticles recruiting hyaluronic acid ligand and polyplexes containing low molecular weight protamine and ATP-Sensitive DNA motif for doxorubicin delivery. J Drug Deliv Sci Technol. 2022;69:103169. doi: 10.1016/j.jddst.2022.103169.
64. Chatterjee S, Lou XY, Liang F, Yang YW. Surface functionalized gold and silver nanoparticles for colorimetric and fluorescent sensing of metal ions and biomolecules. Coordination Chemistry Reviews. 2022;459:214461. doi: 10.1016/j.ccr.2022.214461.
65. Zhang X, Yao M, Chen M, Li L, Dong C, Hou Y. Hyaluronic acid coated silver nanoparticles as a nanoplatform for in vivo imaging applications. ACS Appl Mater Interfaces. 2016;8(39):25650-3. doi: 10.1021/acsami.6b08166, PMID 27645123.
66. Mohseni Dargah M, Falahati Z, Dabirmanesh B, Nasrollahi P, Khajeh K. Machine learning in surface plasmon resonance for environmental monitoring. In: Artificial intelligence and data science in environmental sensing. Elsevier; 2022. p. 269-98. doi: 10.1016/B978-0-323-90508-4.00012-5.
67. Rezic I, Somogyi Skoc MJ. Computational methodologies in synthesis preparation and application of antimicrobial polymers biomolecules and nanocomposites. Polymers (Basel). 2024;16(16):2320. doi: 10.3390/polym16162320, PMID 39204538.
68. Matalqah S, Lafi Z, Mhaidat Q, Asha N, Yousef Asha S. Applications of machine learning in liposomal formulation and development. Pharm Dev Technol. 2025;30(1):126-36. doi: 10.1080/10837450.2024.2448777.
69. Jia Y, Hou X, Wang Z, Hu X. Machine learning boosts the design and discovery of nanomaterials. ACS Sustainable Chemistry & Engineering. 2021;9(18):6130-47. doi: 10.1021/acssuschemeng.1c00483.
70. Konstantopoulos G, Koumoulos EP, Charitidis CA. Digital innovation enabled nanomaterial manufacturing; machine learning strategies and green perspectives. Nanomaterials (Basel). 2022;12(15):2646. doi: 10.3390/nano12152646, PMID 35957077.
71. Bekoz Ullen N, Karabulut G. Karakus SJJoME, performance. Assessing the surface properties of plant extract-based silver nanoparticle coatings on 17-4 PH stainless steel foams using artificial intelligence-supported RGB analysis: a comparative study. Journal of Materials Engineering and Performance 2023;32(23):10637-54. doi: 10.1007/s11665-023-08322-5.
72. Winkler DA, Burden FR, Yan B, Weissleder R, Tassa C, Shaw S. Modelling and predicting the biological effects of nanomaterials. SAR QSAR Environ Res. 2014;25(2):161-72. doi: 10.1080/1062936X.2013.874367, PMID 24625316.
73. Mirzaei M, Furxhi I, Murphy F, Mullins M. A machine learning tool to predict the antibacterial capacity of nanoparticles. Nanomaterials (Basel). 2021;11(7):1774. doi: 10.3390/nano11071774, PMID 34361160.
74. Alavi M, Kowalski R, Capasso R, Douglas Melo Coutinho H, Rose Alencar De Menezes. Various novel strategies for functionalization of gold and silver nanoparticles to hinder drug resistant bacteria and cancer cells. Micro Nano Bio Aspects. 2022;1(1):38-48. doi: 10.22034/mnba.2022.152629.
75. Yang Y, Wang K, Liu X, Xu C, You Q, Zhang Y. Environmental behavior of silver nanomaterials in aquatic environments: an updated review. The Science of the Total Environment. 2023;907(26)167861. doi: 10.1016/j.scitotenv.2023.167861.
76. Oluwagbade E. Integrating explainable AI techniques to enhance transparency in predictive maintenance models; 2025.
77. Tun WS, Talodthaisong C, Daduang S, Daduang J, Rongchai K, Patramanon R. A machine learning colorimetric biosensor based on acetylcholinesterase and silver nanoparticles for the detection of dichlorvos pesticides. Mater Chem Front. 2022;6(11):1487-98. doi: 10.1039/D2QM00186A.
78. Prasad A, Santra TS, Jayaganthan RJ. A study on prediction of size and morphology of ag nanoparticles using machine learning models for biomedical applications. Metals. 2024;14(5):539. doi: 10.3390/met14050539.
79. Yaqub ZT, Oboirien BO. Machine learning applications for nano-synthesized materials production and utilization. In: Nanomaterials for sustainable hydrogen production and storage. CRC Press; 2024. p. 123-35. doi: 10.1201/9781003371007-7.
80. Shrivas K, Wu HF. Applications of silver nanoparticles capped with different functional groups as the matrix and affinity probes in surface-assisted laser desorption/ionization time-of-flight and atmospheric pressure matrix-assisted laser desorption/ionization ion trap mass spectrometry for rapid analysis of sulfur drugs and biothiols in human urine. Rapid Commun Mass Spectrom. 2008;22(18):2863-72. doi: 10.1002/rcm.3681.
81. Tao H, Wu T, Aldeghi M, Wu TC, Aspuru Guzik A, Kumacheva E. Nanoparticle synthesis assisted by machine learning. Nature Reviews Materials. 2021;6(8):701-16. doi: 10.1038/s41578-021-00337-5.
82. Verma SK, Nandi A, Simnani FZ, Singh D, Sinha A, Naser SS. In silico nanotoxicology: the computational biology state of art for nanomaterial safety assessments. Materials & Design. 2023;235:112452. doi: 10.1016/j.matdes.2023.112452.
83. Mao BH, Luo YK, Wang BJ, Chen CW, Cheng FY, Lee YH. Use of an in silico knowledge discovery approach to determine mechanistic studies of silver nanoparticles induced toxicity from in vitro to in vivo. Part Fibre Toxicol. 2022;19(1):6. doi: 10.1186/s12989-022-00447-0, PMID 35031062.
84. Masarkar A, Maparu AK, Nukavarapu YS, Rai B. Predicting cytotoxicity of nanoparticles: a meta-analysis using machine learning. ACS Appl Nano Mater. 2024;7(17):19991-20002. doi: 10.1021/acsanm.4c02269.
85. Bilgi E, Karakus CO. Machine learning-assisted prediction of the toxicity of silver nanoparticles: a meta-analysis. Journal of Nanoparticle Research. 2023;25(8):157. doi: 10.1007/s11051-023-05806-2.
86. Turan NB, Erkan HS, Engin GO, Bilgili MS. Nanoparticles in the aquatic environment: usage properties transformation and toxicity a review. 2019;130:238-49. doi: 10.1016/j.psep.2019.08.014.
87. Fahmy HM, Mosleh AM, Elghany AA, Shams Eldin E, Abu Serea ES, Ali SA. Coated silver nanoparticles: synthesis cytotoxicity and optical properties. RSC Adv. 2019;9(35):20118-36. doi: 10.1039/c9ra02907a, PMID 35514687.
88. Alshaer W, Zraikat M, Amer A, Nsairat H, Lafi Z, Alqudah DA. Encapsulation of echinomycin in cyclodextrin inclusion complexes into liposomes: in vitro anti-proliferative and anti-invasive activity in glioblastoma. RSC Adv. 2019;9(53):30976-88. doi: 10.1039/c9ra05636j, PMID 35529392.
89. Reidy B, Haase A, Luch A, Dawson KA, Lynch IJ. Mechanisms of silver nanoparticle release transformation and toxicity: a critical review of current knowledge and recommendations for future studies and applications. Materials (Basel). 2013;6(6):2295-350. doi: 10.3390/ma6062295, PMID 28809275.
90. Desai N, Pande S, Salave S, Singh TR, Vora LK. Antitoxin nanoparticles: design considerations functional mechanisms and applications in toxin neutralization. Drug Discovery Today. 2024;29(8):104060. doi: 10.1016/j.drudis.2024.104060.
91. Weldon BA. Tarnished: the toxic potential of silver nanoparticles; 2016.
92. Leong SX, Leong YX, Koh CS, Tan EX, Nguyen LB, Chen JR. Emerging nanosensor platforms and machine learning strategies toward rapid point-of-need small molecule metabolite detection and monitoring. Chem Sci. 2022;13(37):11009-29. doi: 10.1039/d2sc02981b, PMID 36320477.
93. Lwakatare LE, Raj A, Crnkovic I, Bosch J, Olsson HH. Large-scale machine learning systems in real-world industrial settings: a review of challenges and solutions. Information and Software Technology. 2020;127:106368. doi: 10.1016/j.infsof.2020.106368.
94. Hughes A, Liu Z, Reeves ME. PAME: plasmonic assay modeling environment. PeerJ Computer Science. 2015;1:e17. doi: 10.7717/peerj-cs.17.
95. Sun B, Fernandez M, Barnard AS. Machine learning for silver nanoparticle electron transfer property prediction. J Chem Inf Model. 2017;57(10):2413-23. doi: 10.1021/acs.jcim.7b00272, PMID 28938072.
96. Mirzaei M, Furxhi I, Murphy F, Mullins M. A machine learning tool to predict the antibacterial capacity of nanoparticles. Nanomaterials (Basel). 2021;11(7):1774. doi: 10.3390/nano11071774, PMID 34361160.
97. Desai AS, Ashok A, Edis Z, Bloukh SH, Gaikwad M, Patil R. Meta-analysis of cytotoxicity studies using machine learning models on physical properties of plant extract derived silver nanoparticles. Int J Mol Sci. 2023;24(4):4220. doi: 10.3390/ijms24044220, PMID 36835640, PMCID PMC9966579.
98. Furxhi I, Faccani L, Zanoni I, Brigliadori A, Vespignani M, Costa AL. Design rules applied to silver nanoparticles synthesis: a practical example of machine learning application. Comput Struct Biotechnol J. 2024;25:20-33. doi: 10.1016/j.csbj.2024.02.010, PMID 38444982, PMCID PMC10914561.
99. Kant K, Beeram R, Cabaleiro LG, Cao Y, Quesada Gonzalez D, Guo H. Road map for plasmonic nanoparticle sensors: current progress challenges and future prospects. Nanoscale Horizons. 2024 Nov 19;9(12):2085-166. doi: 10.1039/d4nh00226a.
100. Tovar Lopez FJ. Recent progress in micro and nanotechnology enabled sensors for biomedical and environmental challenges. Sensors (Basel). 2023;23(12):5406. doi: 10.3390/s23125406, PMID 37420577.
101. Bedia C. Metabolomics in environmental toxicology: applications and challenges. Trends in Environmental Analytical Chemistry. 2022;34:e00161. doi: 10.1016/j.teac.2022.e00161.
102. Jia X, Wang T, Zhu HJ. Advanc Comp Toxicol Interpretable Mach Learn. Environmental Science & Technology. 2023;57(46):17690-706. doi: 10.1021/acs.est.3c00653.
103. Singh AV, Ansari MH, Rosenkranz D, Maharjan RS, Kriegel FL, Gandhi K. Artificial intelligence and machine learning in computational nanotoxicology: unlocking and empowering nanomedicine. Adv Healthc Mater. 2020;9(17):e1901862. doi: 10.1002/adhm.201901862, PMID 32627972.
104. Shatkin JA, Ong K, Ede JJ. Minimizing risk: an overview of risk assessment and risk management of nanomaterials protocols and industrial innovations. Metrology and Standardization of Nanotechnology. 2017:381-408. doi: 10.1002/9783527800308.ch24.
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