Abstract
Almost 40 years since Eric Drexler’s 1991 MIT dissertation, “Molecular Machinery and Manufacturing with Applications to Computation” (Drexler, Nanosystems: molecular machinery, manufacturing, and computation, Wiley, 1992), a lot of exciting has happened in nanotechnology. By far, this nascent field of technology in combination with biotechnology has contributed heavily in medicine and biomaterials R&D sector, but nanomaterials for “green applications” are still quite away from realization. There are several issues underlying this delay, and these can be classified into two broad categories: (i) issues intrinsic to nanomaterials: associated with sustainable tailor-made production, optimization, scale-up, and stability and (ii) environmental issues of nanomaterials associated with their interaction and safe disposal. To have further clarity on first category above, the major intrinsic issues of nanomaterials are grouped into six attributes of studies. These are composition, synthesis, internal and external properties, stability, toxicity, and lifecycle assessment. Although these appear independent attributes but actually they interact with each other and a term “Ensemble Heterogeneity” (he) is defined here to understand their interdependence. Machine learning (ML) can provide deeper insights into both visible and hidden levels of these interdependencies. In the present chapter, firstly, these six attributes are reviewed and an understanding is developed that these attributes must be studied for hidden parameters and patterns using ML for producing optimized design solutions for nanomaterials. Secondly, an overview of different types of ML approaches is given that can contribute to the development of optimized design of nanomaterials. Finally, life cycle assessment (LCA) of nanomaterials is discussed. LCA is a tool to assess the environmental impact and sustainability of any technology to be “green.”
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References
Abbasi E, Aval SF, Akbarzadeh A et al (2014) Dendrimers: synthesis, applications, and properties. Nanoscale Res Lett 9:1–10. https://doi.org/10.1186/1556-276X-9-247
Abdalla AM, Hossain S, Azad AT et al (2018) Nanomaterials for solid oxide fuel cells: a review. Renew Sustain Energy Rev 82:353
Adir O, Poley M, Chen G et al (2019) Integrating artificial intelligence and nanotechnology for precision cancer medicine. Adv Mater 1901989:1–15. https://doi.org/10.1002/adma.201901989
Afantitis A, Melagraki G, Isigonis P et al (2020) NanoSolveIT project: driving nanoinformatics research to develop innovative and integrated tools for in silico nanosafety assessment. Comput Struct Biotechnol J 18:583–602
Ahmadi R, Razzaghian A, Eivazi Z, Shahidi K (2018) Synthesis of Cu-CuO and Cu-Cu2O nanoparticles via electro-explosion of wire method. Int J Nanosci Nanotechnol 14:93–99
Alfaro JF, Sharp BE, Miller SA (2010) Developing LCA techniques for emerging systems: game theory, agent modeling as prediction tools. In: Proceedings of the 2010 IEEE international symposium on sustainable systems and technology. IEEE, pp 1–6
Ali J, Ali N, Wang L et al (2019) Revisiting the mechanistic pathways for bacterial mediated synthesis of noble metal nanoparticles. J Microbiol Methods 159:18
Amendola V, Meneghetti M (2013) What controls the composition and the structure of nanomaterials generated by laser ablation in liquid solution? Phys Chem Chem Phys 15:3027–3046. https://doi.org/10.1039/C2CP42895D
Ao Y, Li H, Zhu L et al (2019) The linear random forest algorithm and its advantages in machine learning assisted logging regression modeling. J Pet Sci Eng 174:776. https://doi.org/10.1016/j.petrol.2018.11.067
Arvidsson R (2015) Life cycle assessment and risk assessment of manufactured Nanomaterials. In: Dolez PI (ed) Nanoengineering. Elsevier, pp 225–256
Aşik E, Akpinar Y, Tülin Güray N et al (2016) Cellular uptake, genotoxicity and cytotoxicity of cobalt ferrite magnetic nanoparticles in human breast cells. Toxicol Res (Camb) 5:1649. https://doi.org/10.1039/c6tx00211k
Atkeson CG, Moore AW, Schaal S (1997) Locally weighted learning for control. Artif Intell Rev 11:75–113
Awad M, Khanna R (2015) Efficient learning machines: theories, concepts, and appplications for engineers and system designers. Springer, pp 1–248. https://doi.org/10.1007/978-1-4302-5990-9
Bafana A, Kumar SV, Temizel-Sekeryan S et al (2018) Evaluating microwave-synthesized silver nanoparticles from silver nitrate with life cycle assessment techniques. Sci Total Environ 636:936–943. https://doi.org/10.1016/j.scitotenv.2018.04.345
Bai X, Liu F, Liu Y et al (2017) Toward a systematic exploration of nano-bio interactions. Toxicol Appl Pharmacol 323:66–73. https://doi.org/10.1016/j.taap.2017.03.011
Bajpai VK, Kamle M, Shukla S et al (2018) Prospects of using nanotechnology for food preservation, safety, and security. J Food Drug Anal 26:1201
Baumann H, Tillmann A-M (2004) LCA in a nutshell. In: The Hitch Hiker’s Guide to LCA. An orientation in life cycle assessment methodology and application, Studentlitteratur, Lund, Sweden, ISBN 91-44-02364-2
Beernaert D, Fribourg-Blanc E (2017) Thirty years of cooperative research and innovation in Europe: the case for micro- and nanoelectronics and smart systems integration. In: Nanoelectronics: materials, devices, applications, 567-594, Wiley‐VCH Verlag GmbH & Co. KGaA.
Belchi R, Habert A, Foy E et al (2019) One-step synthesis of TiO2/Graphene Nanocomposites by laser pyrolysis with well-controlled properties and application in Perovskite solar cells. ACS Omega 4:11,906–11,913. https://doi.org/10.1021/acsomega.9b01352
Bello SA, Agunsoye JO, Hassan SB (2015) Synthesis of coconut shell nanoparticles via a top down approach: assessment of milling duration on the particle sizes and morphologies of coconut shell nanoparticles. Mater Lett 159:514–519. https://doi.org/10.1016/j.matlet.2015.07.063
Berrar D (2018) Bayes’ theorem and naive Bayes classifier. Encycl Bioinforma Comput Biol ABC Bioinforma 1–3:403–412. https://doi.org/10.1016/B978-0-12-809633-8.20473-1
Bharat TC, Shubham, Mondal S et al (2019) Synthesis of doped zinc oxide nanoparticles: a review. Mater Today Proc 11:767–775
Bhatia S. (2016) Nanoparticles Types, Classification, Characterization, Fabrication Methods and Drug Delivery Applications. In: Natural Polymer Drug Delivery Systems. Springer, Cham. https://doi.org/10.1007/978-3-319-41129-3_2
Brown KA, Brittman S, Maccaferri N et al (2020) Machine learning in nanoscience: big data at small scales. Nano Lett 20:2–10. https://doi.org/10.1021/acs.nanolett.9b04090
Bürgi BR, Pradeep T (2006) Societal implications of nanoscience and nanotechnology in developing countries. Curr Sci 90:645–658
Camboni M, Hanlon J, García RP, Floyd P (2019) A state of play study of the market for so called “next generation” nanomaterials. European Chemical Agency, Helsinki, Finland
Caruthers JM, Lauterbach JA, Thomson KT et al (2003) Catalyst design: knowledge extraction from high-throughput experimentation. J Catalysis 216:98–109
Casals E, Gusta MF, Piella J et al (2017) Intrinsic and extrinsic properties affecting innate immune responses to nanoparticles: the case of cerium oxide. Front Immunol 8:970
Cellulose Ether Market—Global Industry Analysis and Forecast (2016–2024). https://www.transparencymarketresearch.com/nano-biotechnology-market.html. Accessed 8 Jun 2020
Chatzimitakos TG, Stalikas CD (2019) Carbon nanodots from natural (re)sources: a new perspective on analytical chemistry. In: Handbook of nanomaterials in analytical chemistry: modern trends in analysis. Elsevier, pp 3–28
Cheeseman S, Christofferson AJ, Kariuki R et al (2020) Antimicrobial metal Nanomaterials: from passive to stimuli-activated applications. Adv Sci 7:1902913. https://doi.org/10.1002/advs.201902913
Chen YC, Huang XC, Luo YL et al (2013) Non-metallic nanomaterials in cancer theranostics: a review of silica- and carbon-based drug delivery systems. Sci Technol Adv Mater 14:14. https://doi.org/10.1088/1468-6996/14/4/044407
Chen P, Tang Z, Zeng Z et al (2020) Machine-learning-guided morphology engineering of nanoscale metal-organic frameworks. Matter 2:1651. https://doi.org/10.1016/j.matt.2020.04.021
Chollet F (2017) Deep learning with Python. Manning Publications, Shelter Island
Chong WC, Chung YT, Teow YH et al (2018) Environmental impact of nanomaterials in composite membranes: life cycle assessment of algal membrane photoreactor using polyvinylidene fluoride—composite membrane. J Clean Prod 202:591. https://doi.org/10.1016/j.jclepro.2018.08.121
Collins JE, Bell H (2014) Intelligent material. In: Technical proceedings of the 2014 NSTI nanotechnology conference and expo, NSTI-Nanotech 2014
Cucurachi S, Blanco Rocha CF (2019) Life-cycle assessment of engineered nanomaterials. In: Pacheco-Torgal F et al (eds) Nanotechnology in eco-efficient construction. Woodhead Publishing, Cambridge, pp 815–846
Cucurachi S, Van Der Giesen C, Guinée J (2018) Ex-ante LCA of emerging technologies. Proc CIRP 69:463
Curran MA (2012) Life cycle assessment handbook: a guide for environmentally sustainable products. Wiley
Dasgupta N, Ranjan S, Ramalingam C (2017) Applications of nanotechnology in agriculture and water quality management. Environ Chem Lett 15:591
De Morais MG, Martins VG, Steffens D et al (2014) Biological applications of nanobiotechnology. J Nanosci Nanotechnol 14:1007–1017. https://doi.org/10.1166/jnn.2014.8748
Decarolis D, Odarchenko Y, Herbert JJ et al (2020) Identification of the key steps in the self-assembly of homogeneous gold metal nanoparticles produced using inverse micelles. Phys Chem Chem Phys 22:18,824. https://doi.org/10.1039/c9cp03473k
Di Sia P (2017) Nanotechnology among innovation, health and risks. Procedia Soc Behav Sci 237:1076–1080. https://doi.org/10.1016/j.sbspro.2017.02.158
Dorogush, A. V., Ershov, V., & Gulin, A. (2018). CatBoost: gradient boosting with categorical features support. arXiv:1810.11363.
Drexler, KE (2006). Engines of creation 2.0. The Coming Era of Nanotechnology. Anchor Books- Doubleday, 1986, 576. Retrieved from http://www1.appstate.edu/dept/physics/nanotech/EnginesofCreation2_8803267.pdf
Duan Y, Coreas R, Liu Y et al (2020) Prediction of protein corona on nanomaterials by machine learning using novel descriptors. NanoImpact 17:100207. https://doi.org/10.1016/j.impact.2020.100207
Ealias AM, Saravanakumar MP (2017) A review on the classification, characterisation, synthesis of nanoparticles and their application. In: IOP Conf. Series: Materials Science and Engineering 263 (2017) 032019. https://doi.org/10.1088/1757-899X/263/3/032019
ECHA (2019) Appendix R. 6–1 for nanoforms applicable to the guidance on QSARs and grouping of chemicals. https://doi.org/10.2823/273911
Elhami B, Khanali M, Akram A (2017) Combined application of artificial neural networks and life cycle assessment in lentil farming in Iran. Inf Process Agric 4:18. https://doi.org/10.1016/j.inpa.2016.10.004
European Chemicals Agency (2017) Guidance on information requirements and chemical safety assessment: appendix R.6-1 for nanomaterials applicable to the guidance on QSARs and grouping of chemicals. Version 10, p 1–29. https://doi.org/10.2823/884050
Fernandez M, Barron H, Barnard AS (2017) Artificial neural network analysis of the catalytic efficiency of platinum nanoparticles. RSC Adv 7:48,962. https://doi.org/10.1039/c7ra06622h
Fiori G, Bonaccorso F, Iannaccone G et al (2014) Electronics based on two-dimensional materials. Nat Nanotechnol 9:768–779
Furxhi I, Murphy F, Mullins M et al (2020) Practices and trends of machine learning application in Nanotoxicology. Nano 10:116. https://doi.org/10.3390/nano10010116
Gao Y, Kang J, Lei Z et al (2020) Use of the highly biocompatible Au nanocages@PEG nanoparticles as a new contrast agent for in vivo computed tomography scan imaging. Nanoscale Res Lett 15:15. https://doi.org/10.1186/s11671-020-3286-2
Gatoo MA, Naseem S, Arfat MY et al (2014) Physicochemical properties of nanomaterials: implication in associated toxic manifestations. Toxicity Nanomater 2014:1. https://doi.org/10.1155/2014/498420
Gavankar S, Suh S, Keller AA (2015) The role of scale and technology maturity in life cycle assessment of emerging technologies: a case study on carbon nanotubes. J Ind Ecol 19:51. https://doi.org/10.1111/jiec.12175
Geman S, Bienenstock E, Doursat R (1992) Neural networks and the bias/variance dilemma. Neural Comput 4:1–58
Géron A (2017) Hands-on machine learning with Scikit-learn and TensorFlow: concepts, tools, and techniques to build intelligent systems, 2nd Edition, O'Reilly Media, Inc. ISBN: 9781492032649
Geyer N, Wollschläger N, Fuhrmann B et al (2015) Influence of the doping level on the porosity of silicon nanowires prepared by metal-assisted chemical etching. Nanotechnology 26:26. https://doi.org/10.1088/0957-4484/26/24/245301
Ghahramani Z. (2004) Unsupervised Learning. In: Bousquet O., von Luxburg U., Rätsch G. (eds) Advanced Lectures on Machine Learning. ML 2003. Lecture Notes in Computer Science, vol 3176. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-28650-9_5
Gicheva G, Yordanov G (2013) Environmental impact of nanomaterials. In book: Colloid and interface chemistry nanotechnology, pp 37–57, CRC press, UK.
van der Giesen C, Cucurachi S, Guinée J et al (2020) A critical view on the current application of LCA for new technologies and recommendations for improved practice. J Clean Prod 259:120904. https://doi.org/10.1016/j.jclepro.2020.120904
Golovynskyi, S., Datsenko, O. I., Seravalli, L., Trevisi, G., Frigeri, P., Babichuk, I. S., ... & Qu, J. (2019). Defect influence on in-plane photocurrent of InAs/InGaAs quantum dot array: long-term electron trapping and Coulomb screening. Nanotechnology, 30(30), 305701.
Gonçalves, M. C., & Margarido, F. (2015). Materials for construction and civil engineering. Springer International Publishing, Cham.
Görnitz N, Kloft M, Rieck K, Brefeld U (2013) Toward supervised anomaly detection. J Artif Intell Res 46:235–262. https://doi.org/10.1613/jair.3623
Grandvalet Y, Bengio Y (2005) Semi-supervised learning by entropy minimization. In Conference: Advances in Neural Information Processing Systems 17, Vancouver, British Columbia, Canada
Grimaldi F, Pucciarelli M, Gavriilidis A et al (2020) Anticipatory life cycle assessment of gold nanoparticles production: comparison of milli-continuous flow and batch synthesis. J Clean Prod 269:122335. https://doi.org/10.1016/j.jclepro.2020.122335
Guo X, Li Y, Yan J et al (2016) Size- and coating-dependent cytotoxicity and genotoxicity of silver nanoparticles evaluated using in vitro standard assays. Nanotoxicology 10:1373. https://doi.org/10.1080/17435390.2016.1214764
Hamad AF, Han JH, Kim BC, Rather IA (2018) The intertwine of nanotechnology with the food industry. Saudi J Biol Sci 25:27
Han Y, Li Q, Ng KW et al (2018) InGaAs/InP quantum wires grown on silicon with adjustable emission wavelength at telecom bands. Nanotechnology 29:225601. https://doi.org/10.1088/1361-6528/aab53b
He X, Deng H, Hwang H (2019) The current application of nanotechnology in food and agriculture. J Food Drug Anal 27:1
Hernández-Muñoz P, Cerisuelo JP, Domínguez I, et al (2018) Nanotechnology in food packaging. In: Micro and Nano Technologies, Nanomaterials for Food Applications, Elsevier, 2019, 205–232, ISBN 9780128141304, https://doi.org/10.1016/B978-0-12-814130-4.00008-7
Hijazi O, Abdelsalam E, Samer M et al (2020) Life cycle assessment of the use of nanomaterials in biogas production from anaerobic digestion of manure. Renew Energy 148:417–424. https://doi.org/10.1016/j.renene.2019.10.048
Hosseinzadeh-Bandbafha H, Tabatabaei M, Aghbashlo M et al (2020) Data supporting consolidating emission indices of a diesel engine powered by carbon nanoparticle-doped diesel/biodiesel emulsion fuels using life cycle assessment framework. Data Br 30:105428. https://doi.org/10.1016/j.dib.2020.105428
Hou P, Jolliet O, Zhu J, Xu M (2020) Estimate ecotoxicity characterization factors for chemicals in life cycle assessment using machine learning models. Environ Int 135:105393. https://doi.org/10.1016/j.envint.2019.105393
Hu L, Cui Y (2012) Energy and environmental nanotechnology in conductive paper and textiles. Energy Environ Sci 5:6423
Hutchison JE (2008) Greener nanoscience: a proactive approach to advancing applications and reducing implications of nanotechnology. ACS Nano 2:395–402. https://doi.org/10.1021/nn800131j
International Organization for Standardization (ISO) (2010) ISO/TR 11360:2010—Nanotechnologies—Methodology for the classification and categorization of nanomaterials
Iravani S (2011) Green synthesis of metal nanoparticles using plants. Green Chem 13:2638–2650. https://doi.org/10.1039/c1gc15386b
Jeevanandam J, Barhoum A, Chan YS et al (2018) Review on nanoparticles and nanostructured materials: history, sources, toxicity and regulations. Beilstein J Nanotechnol 9:1050–1074
Kadavil H, Zagho M, Elzatahry A, Altahtamouni T (2019) Sputtering of electrospun polymer-based nanofibers for biomedical applications: a perspective. Nanomaterials 9:9. https://doi.org/10.3390/nano9010077
Kakoty H, Banerjee R, Dasgupta C, Ghosh A (2016) Role of entropy in the expulsion of dopants from optically trapped colloidal assemblies. Phys Rev Lett 117:258002. https://doi.org/10.1103/PhysRevLett.117.258002
Kalinin SV, Sumpter BG, Archibald RK (2015) Big–deep–smart data in imaging for guiding materials design. Nat Mater 14:973–980. https://doi.org/10.1038/nmat4395
Karak SK, Chatterjee S, Bandopadhyay S (2015) Mathematical modelling of the physical and mechanical properties of nano-Y2O3 dispersed ferritic alloys using evolutionary algorithm-based neural network. Powder Technol 274:217. https://doi.org/10.1016/j.powtec.2015.01.028
Kate M, Lane MMKM, Zimmerman JB (2019) Controlling metal oxide nanoparticle size and shape with supercritical fluid synthesis CRITICAL REVIEW controlling metal oxide nanoparticle size and shape with supercritical fluid synthesis. Green Chem 21:3769–3781. https://doi.org/10.1039/x0xx00000x
Katelhön A, Bardow A, Suh S (2016) Stochastic technology choice model for consequential life cycle assessment. Environ Sci Technol 50:12,575. https://doi.org/10.1021/acs.est.6b04270
Kavitha S, Varuna S, Ramya R (2016) A comparative analysis on linear regression and support vector regression. In: 2016 online international conference on green engineering and technologies (IC-GET). IEEE, pp 1–5
Khan I, Saeed K, Khan I (2019) Nanoparticles: properties, applications and toxicities. Arab J Chem 12:908–931
Koch D, Paul M, Beisl S et al (2020) Life cycle assessment of a lignin nanoparticle biorefinery: decision support for its process development. J Clean Prod 245:118760. https://doi.org/10.1016/j.jclepro.2019.118760
Konar A (2005) Supervised neural learning algorithms. In: Konar A (ed) Computational intelligence. Springer, Berlin Heidelberg, pp 197–235
Kopp M, Kollenda S, Epple M (2017) Nanoparticle-protein interactions: therapeutic approaches and supramolecular chemistry. Acc Chem Res 50:1383–1390. https://doi.org/10.1021/acs.accounts.7b00051
Lauterwasser C (2014) Opportunities and risks of nanotechnologies report in co-operation with the OECD international futures Programme, Allianz-Aktiengesellschaft Contributor OECD. publisher Allianz Center for Technology.
Lee W-M (2019) Supervised learning-classification using logistic regression. In: Lee W-M (ed) Python® machine learning. John Wiley & Sons, pp 151–175
Li Y, Pu Q, Li S et al (2019) Machine learning methods for research highlight prediction in biomedical effects of nanomaterial application. Pattern Recogn Lett 117:111. https://doi.org/10.1016/j.patrec.2018.11.008
Lin D, Liu Y, Cui Y (2017) Reviving the lithium metal anode for high-energy batteries. Nat Nanotechnol 12:194
Liu S, Serrano D, Fossati A et al (2018) Controlled size reduction of rare earth doped nanoparticles for optical quantum technologies. RSC Adv 8:37,098–37,104. https://doi.org/10.1039/C8RA07246A
López-Andrés JJ, Aguilar-Lasserre AA, Morales-Mendoza LF et al (2018) Environmental impact assessment of chicken meat production via an integrated methodology based on LCA, simulation and genetic algorithms. J Clean Prod 174:477. https://doi.org/10.1016/j.jclepro.2017.10.307
Lord AM, Ramasse QM, Kepaptsoglou DM et al (2017) Stability of Schottky and Ohmic au Nanocatalysts to ZnO nanowires. Nano Lett 17:6626–6636. https://doi.org/10.1021/acs.nanolett.7b02561
Louppe G (2014) Understanding random forests: from theory to practice: arXiv:1407.7502
Luo G, Du L, Wang K (2015) Encyclopedia of microfluidics and nanofluidics. Encycl Microfluid Nanofluidics. https://doi.org/10.1007/978-1-4614-5491-5
Ma Y, Yang M, Yuan F, Wu X (2019) A review on heterogeneous nanostructures: a strategy for superiormechanical properties in metals. Metals (Basel) 9:14–17. https://doi.org/10.3390/met9050598
Marimón-Bolívar W, González EE (2018) Green synthesis with enhanced magnetization and life cycle assessment of Fe 3 O 4 nanoparticles. Environ Nanotechnol Monit Manag 9:58–66. https://doi.org/10.1016/j.enmm.2017.12.003
McMillin KW (2017) Advancements in meat packaging. Meat Sci 132:153
Mobasser, S., & Firoozi, A. A. (2016). Review of nanotechnology applications in science and engineering. J Civil Eng Urban, 6(4), 84-93.
Mody V, Siwale R, Singh A, Mody H (2010) Introduction to metallic nanoparticles. J Pharm Bioallied Sci 2:282. https://doi.org/10.4103/0975-7406.72127
Moni SM, Mahmud R, High K, Carbajales-Dale M (2020) Life cycle assessment of emerging technologies: a review. J Ind Ecol 24:52. https://doi.org/10.1111/jiec.12965
Morales EF, Zaragoza JH (2011) An introduction to reinforcement learning. In: Sucar LE, Morales EF, Hoey J (eds) Decision theory models for applications in artificial intelligence: concepts and solutions. IGI Global, pp 63–80. https://doi.org/10.4018/978-1-60960-165-2.ch004
Moro C, Francioso V, Schrager M, Velay-Lizancos M (2020) TiO2 nanoparticles influence on the environmental performance of natural and recycled mortars: a life cycle assessment. Environ Impact Assess Rev 84:106,430. https://doi.org/10.1016/j.eiar.2020.106430
Nakayama M, Smith CL, Feltis BN et al (2020) Samarium doped titanium dioxide nanoparticles as theranostic agents in radiation therapy. Phys Med 75:69–76. https://doi.org/10.1016/j.ejmp.2020.06.007
Nentwich A (2011) Production of nanoparticles and nanomaterials. Planet-AustriaAt 6:1–4. https://doi.org/10.1553/ITA-nt-006en
Ouyang T, Ye YQ, Wu CY et al (2019) Heterostructures composed of N-doped carbon nanotubes encapsulating cobalt and β-Mo 2 C nanoparticles as Bifunctional electrodes for water splitting. Angew Chemie Int Ed 58:4923–4928. https://doi.org/10.1002/anie.201814262
Ozbilen A, Aydin M, Dincer I, Rosen MA (2013) Life cycle assessment of nuclear-based hydrogen production via a copper-chlorine cycle: a neural network approach. Int J Hydrog Energy 38:6314. https://doi.org/10.1016/j.ijhydene.2013.03.071
Parashar M, Shukla VK, Singh R (2020) Metal oxides nanoparticles via sol–gel method: a review on synthesis, characterization and applications. J Mater Sci Mater Electron 31:3729–3749
Park JH, Kissinger S, Ra YH et al (2014) Horizontal assembly of single nanowire diode fabricated by p-n junction GaN nw grown by MOCVD. J Nanomater 2014:951360. https://doi.org/10.1155/2014/951360
Parra-Robert M, Casals E, Massana N et al (2019) Beyond the scavenging of reactive oxygen species (Ros): direct effect of cerium oxide nanoparticles in reducing fatty acids content in an in vitro model of hepatocellular steatosis. Biomol Ther 9. https://doi.org/10.3390/biom9090425
Perde-Schrepler M, Florea A, Brie I et al (2019) Size-dependent cytotoxicity and genotoxicity of silver nanoparticles in cochlear cells in vitro. J Nanomater 2019:1. https://doi.org/10.1155/2019/6090259
Phan HT, Haes AJ (2019) What does nanoparticle stability mean? HHS public access. J Phys Chem C Nanomater Interfaces 123:16,495–16,507. https://doi.org/10.1021/acs.jpcc
Prasad Yadav T, Manohar Yadav R, Pratap Singh D (2012) Mechanical milling: a top down approach for the synthesis of nanomaterials and nanocomposites. Nanosci Nanotechnol 2:22–48. https://doi.org/10.5923/j.nn.20120203.01
Prasad R (2019) Plant Nanobionics: Approaches in Nanoparticles Biosynthesis and Toxicity. Springer International Publishing (ISBN 978-3-030-16379-2) https://www.springer.com/gp/book/9783030163785
Prasad R, Kumar V, Prasad KS (2014) Nanotechnology in sustainable agriculture: present concerns and future aspects. Afr J Biotechnol 13(6):705–713
Prasad R, Pandey R, Barman I (2016) Engineering tailored nanoparticles with microbes: quo vadis. WIREs Nanomed Nanobiotechnol 8:316–330. https://doi.org/10.1002/wnan.1363
Prasad R, Bhattacharyya A, Nguyen QD (2017a) Nanotechnology in sustainable agriculture: Recent developments, challenges, and perspectives. Front Microbiol 8:1014. https://doi.org/10.3389/fmicb.2017.01014
Prasad R, Kumar M, Kumar V (2017b) Nanotechnology: An Agriculture paradigm. Springer Nature Singapore Pte Ltd. (ISBN: 978-981-10-4573-8)
Prasad R, Kumar V and Kumar M (2017c) Nanotechnology: Food and Environmental Paradigm. Springer Nature Singapore Pte Ltd. (ISBN 978-981-10-4678-0)
Pratsinis SE (2012) Design of nanomaterial synthesis by aerosol processes. Annu Rev Chem Biomol Eng 3:103–127. https://doi.org/10.1146/annurev-chembioeng-062011-080930
Quinlan JR (1986) Induction of decision trees. Mach Learn 1:81–106. https://doi.org/10.1007/bf00116251
Ramesh S (2013) Sol-gel synthesis and characterization of Ag 3(2+í μí±¥) Al í μí±¥ Ti 4−í μí±¥ O 11+í μí»¿ (0.0 ≤ í μí±¥ ≤ 1.0) nanoparticles. J Nanosci 2013. doi: https://doi.org/10.1155/2013/929321
Ramos AP, Cruz MAE, Tovani CB, Ciancaglini P (2017) Biomedical applications of nanotechnology. Biophys Rev 9:79–89
Ramsden JJ (2016) The nano/bio interface. Nanotechnology:61–90. https://doi.org/10.1016/b978-0-323-39311-9.00010-8
Ratner M (2013) A brief history of molecular electronics. Nat Nanotechnol 8:378
Regonia PR, Pelicano CM, Tani R et al (2020) Predicting the band gap of ZnO quantum dots via supervised machine learning models. Optik (Stuttg) 207:164469. https://doi.org/10.1016/j.ijleo.2020.164469
Renero-Lecuna C, Iturrioz-Rodríguez N, González-Lavado E et al (2019) Effect of size, shape, and composition on the interaction of different nanomaterials with HeLa cells. J Nanomater 2019:1. https://doi.org/10.1155/2019/7518482
Rienzie R, Adassooriya NM (2018) Nanomaterials: ecotoxicity, safety, and public perception. Springer International Publishing
Rigaud N (2008) OECD international futures project on “the bioeconomy to 2030: designing a policy agenda” biotechnology: ethical and social debates, OECD, ISBN:9789264056886 (PDF), https://doi.org/10.1787/9789264056886-en
Roco MC (2011) The long view of nanotechnology development: the national nanotechnology initiative at 10 years. J Nanopart Res 13:427–445
Roco MC, Bainbridge WS (eds) (2001) Societal implications of nanoscience and nanotechnology. Springer Netherlands, Dordrecht
Roco MC, Bainbridge WS (2005) Societal implications of nanoscience and nanotechnology: maximizing human benefit. J Nanopart Res 7:1–13. https://doi.org/10.1007/s11051-004-2336-5
Saboktakin M (2017) The biological and biomedical nanoparticles—synthesis and applications. Adv Mater Sci 2:1–14. https://doi.org/10.15761/ams.1000127
Šahinagić-Isović M., Ćećez M., Ćatović F. (2019) Nanotechnology in Civil Engineering. In: Karabegović I. (eds) New Technologies, Development and Application. NT 2018. Lecture Notes in Networks and Systems, vol 42. Springer, Cham. https://doi.org/10.1007/978-3-319-90893-9_68
Salieri B, Turner DA, Nowack B, Hischier R (2018) Life cycle assessment of manufactured nanomaterials: where are we? NanoImpact 10:108–120. https://doi.org/10.1016/j.impact.2017.12.003
Sammut C, Webb GI (eds) (2010) Encyclopedia of machine learning. Springer US, Boston, MA
Sammut C, Webb GI (eds) (2017) Encyclopedia of machine learning and data mining. Springer US, Boston, MA
Shiao MH, Lai CP, Liao BH, Lin YS (2018) Effect of photoillumination on gold-nanoparticle-assisted chemical etching of silicon. J Nanomater 2018:1–6. https://doi.org/10.1155/2018/5479605
Slotte M, Zevenhoven R (2017) Energy requirements and life cycle assessment of production and product integration of silver, copper and zinc nanoparticles. J Clean Prod 148:948–957. https://doi.org/10.1016/j.jclepro.2017.01.083
Song H, Tanner PA (2010) Doped Nanomaterials and Nanodevices, Vol. 1 (Ed.: W. Chen), American Scientific Publishers, Valencia
Srivastava S, Usmani Z, Atanasov AG, Singh VK, Singh NP, Abdel-Azeem AM, Prasad R, Gupta G, Sharma M, Bhargava A (2021) Biological nanofactories: Using living forms for metal nanoparticle synthesis. Mini-Reviews in Medicinal Chemistry 21(2): 245–265
Stanley S (2014) Biological nanoparticles and their influence on organisms. Curr Opin Biotechnol 28:69–74
Starón A, Dlugosz O, Pulit-Prociak J, Banach M (2020) Analysis of the exposure of organisms to the action of nanomaterials. Materials (Basel) 13:1–18. https://doi.org/10.3390/ma13020349
Subramani, K., Elhissi, A., Subbiah, U., & Ahmed, W. (2019). Introduction to nanotechnology. In Nanobiomaterials in Clinical Dentistry (pp. 3–18). Elsevier. Chicago
Sutton RS, Barto AG (2018). Reinforcement learning: An introduction. MIT press, MIT Press, Cambridge, MA, ISBN: 9780262039246
Tan M, Chen G (2020) Rare earth-doped nanoparticles for advanced in vivo near infrared imaging. In: Benayas A et al (eds) Near infrared-emitting nanoparticles for biomedical applications. Springer International Publishing, Cham, pp 63–81
Tan XQ, Liu JY, Niu JR et al (2018) Recent progress in magnetron sputtering technology used on fabrics. Materials (Basel) 11:1953. https://doi.org/10.3390/ma11101953
Temizel-Sekeryan S, Hicks AL (2020) Global environmental impacts of silver nanoparticle production methods supported by life cycle assessment. Resour Conserv Recycl 156:104676. https://doi.org/10.1016/j.resconrec.2019.104676
Thai NX, Tonezzer M, Masera L et al (2020) Multi gas sensors using one nanomaterial, temperature gradient, and machine learning algorithms for discrimination of gases and their concentration. Anal Chim Acta 1124:85. https://doi.org/10.1016/j.aca.2020.05.015
Tharwat A, Gaber T, Ibrahim A, Hassanien AE (2017) Linear discriminant analysis: a detailed tutorial. AI Commun 30:169–190. https://doi.org/10.3233/AIC-170729
To KT, Truong L, Edwards S et al (2019) Multivariate modeling of engineered nanomaterial features associated with developmental toxicity. NanoImpact 16:100185. https://doi.org/10.1016/j.impact.2019.100185
Tong L, Qiu F, Zeng T et al (2017) Recent progress in the preparation and application of quantum dots/graphene composite materials. RSC Adv 7:47,999–48,018. https://doi.org/10.1039/c7ra08755a
Tripathi, S., Sanjeevi, R., Anuradha, J., Chauhan, D. S., & Rathoure, A. K. (2018). Nano-bioremediation: nanotechnology and bioremediation. In Biostimulation Remediation Technologies for Groundwater Contaminants (pp. 202–219). IGI Global. https://doi.org/10.4018/978-1-5225-4162-2.ch012
Unabia R, Candidato R, Pawłowski L (2018) Current progress in solution precursor plasma spraying of cermets: a review. Metals (Basel) 8:1–18. https://doi.org/10.3390/met8060420
Valencia-Zapata GA, Mejia D, Klimeck G, et al (2017) A statistical approach to increase classification accuracy in supervised learning algorithms. IPSI BgD Trans Internet Res
Venkatasubramanian V (2019) The promise of artificial intelligence in chemical engineering: is it here, finally? AICHE J 65:466–478. https://doi.org/10.1002/aic.16489
Villares M, Işildar A, Mendoza Beltran A, Guinee J (2016) Applying an ex-ante life cycle perspective to metal recovery from e-waste using bioleaching. J Clean Prod 129:315. https://doi.org/10.1016/j.jclepro.2016.04.066
Vinzons LU, Shu L, Yip S et al (2017) Unraveling the morphological evolution and etching kinetics of porous silicon nanowires during metal-assisted chemical etching. Nanoscale Res Lett 12:385. https://doi.org/10.1186/s11671-017-2156-z
Wang W, Sedykh A, Sun H et al (2017) Predicting Nano–bio interactions by integrating nanoparticle libraries and quantitative nanostructure activity relationship modeling. ACS Nano 11:12,641–12,649. https://doi.org/10.1021/acsnano.7b07093
Wang Y, Yang M, Ma X et al (2018) Improved back stress and synergetic strain hardening in coarse-grain/nanostructure laminates. Mater Sci Eng A 727:113–118. https://doi.org/10.1016/j.msea.2018.04.107
Wang M, Wang T, Cai P, Chen X (2019) Nanomaterials discovery and design through machine learning. Small Methods 3:1900025. https://doi.org/10.1002/smtd.201900025
Weber JH, Kambs B, Kettler J et al (2019) Two-photon interference in the telecom C-band after frequency conversion of photons from remote quantum emitters. Nat Nanotechnol 14:23
Webler GD, Zapata MJM, Maciel GS et al (2014) Influence of impurities on the luminescence of erbium doped barium titanate nanophosphors. J Nanomater 3254:57,061
Wender BA, Foley RW, Hottle TA et al (2014) Anticipatory life-cycle assessment for responsible research and innovation. J Responsible Innov 1:200. https://doi.org/10.1080/23299460.2014.920121
Windsor R, Cinelli M, Coles SR (2018) Comparison of tools for the sustainability assessment of nanomaterials. Curr Opin Green Sustain Chem 12:69
Winters-Hilt S, Merat S (2007) SVM clustering. BMC Bioinformatics 8:1–12. https://doi.org/10.1186/1471-2105-8-S7-S18
Wu F, Harper BJ, Harper SL (2017) Differential dissolution and toxicity of surface functionalized silver nanoparticles in small-scale microcosms: impacts of community complexity. Environ Sci Nano 4:359–372. https://doi.org/10.1039/c6en00324a
Xie Y, Kocaefe D, Chen C, Kocaefe Y (2016) Review of research on template methods in preparation of nanomaterials. J Nanomater 2016. https://doi.org/10.1155/2016/2302595
Xin M, Wang Y (2019) Research on image classification model based on deep convolution neural network. EURASIP J Image Video Process 2019:40. https://doi.org/10.1186/s13640-019-0417-8
Xu L, Liang HW, Yang Y, Yu SH (2018) Stability and reactivity: positive and negative aspects for nanoparticle processing. Chem Rev 118:3209–3250. https://doi.org/10.1021/acs.chemrev.7b00208
Yap YK, Zhang D (2015) Physical vapor deposition. In: Encyclopedia of nanotechnology. Springer Netherlands, Dordrecht, pp 1–8
Yaradoddi J.S., Kontro M.H., Ganachari S.V., Sulochana M.B., Agsar D. (2019) Protein Nanotechnology. In: Martínez L., Kharissova O., Kharisov B. (eds) Handbook of Ecomaterials. Springer, Cham. https://doi.org/10.1007/978-3-319-68255-6_192
Yeo CSH, Xie Q, Wang X, Zhang S (2020) Understanding and optimization of thin film nanocomposite membranes for reverse osmosis with machine learning. J Memb Sci 606:118135. https://doi.org/10.1016/j.memsci.2020.118135
Yildirim S, Röcker B (2018) Active packaging. In: Micro and Nano Technologies,Nanomaterials for Food Packaging, Elsevier, 173–202, ISBN 9780323512718, https://doi.org/10.1016/B978-0-323-51271-8.00007-3
Yokel RA, Hancock ML, Cherian B et al (2019) Simulated biological fluid exposure changes nanoceria’s surface properties but not its biological response. Eur J Pharm Biopharm 144:252–265. https://doi.org/10.1016/j.ejpb.2019.09.023
Zhang S, Li X, Zong M et al (2017) Learning k for kNN classification. ACM Trans Intell Syst Technol 8:1. https://doi.org/10.1145/2990508
Zhou XX, Liu JF, Bin JG (2017) Elemental mass size distribution for characterization, quantification and identification of trace nanoparticles in serum and environmental waters. Environ Sci Technol 51:3892–3901. https://doi.org/10.1021/acs.est.6b05539
Zhu X, Liu P, Ge Y et al (2020) MoS2/MWCNTs porous nanohybrid network with oxidase-like characteristic as electrochemical nanozyme sensor coupled with machine learning for intelligent analysis of carbendazim. J Electroanal Chem 862:113940. https://doi.org/10.1016/j.jelechem.2020.113940
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Pareek, A., Zafar, M., Lakshminarayanan, R., Joshi, S.J. (2021). Nanotechnology for Green Applications: How Far on the Anvil of Machine Learning!. In: Sarma, H., Joshi, S.J., Prasad, R., Jampilek, J. (eds) Biobased Nanotechnology for Green Applications. Nanotechnology in the Life Sciences. Springer, Cham. https://doi.org/10.1007/978-3-030-61985-5_1
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