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Current Radiopharmaceuticals

Editor-in-Chief

ISSN (Print): 1874-4710
ISSN (Online): 1874-4729

Mini-Review Article

Current Advancement and Future Prospects: Biomedical Nanoengineering

Author(s): Sonia Singh* and Hrishika Sahani

Volume 17, Issue 2, 2024

Published on: 06 December, 2023

Page: [120 - 137] Pages: 18

DOI: 10.2174/0118744710274376231123063135

Price: $65

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Abstract

Recent advancements in biomedicine have seen a significant reliance on nanoengineering, as traditional methods often fall short in harnessing the unique attributes of biomaterials. Nanoengineering has emerged as a valuable approach to enhance and enrich the performance and functionalities of biomaterials, driving research and development in the field. This review emphasizes the most prevalent biomaterials used in biomedicine, including polymers, nanocomposites, and metallic materials, and explores the pivotal role of nanoengineering in developing biomedical treatments and processes. Particularly, the review highlights research focused on gaining an in-depth understanding of material properties and effectively enhancing material performance through molecular dynamics simulations, all from a nanoengineering perspective.

Graphical Abstract

[1]
Pijeira, M.S.O.; Viltres, H.; Kozempel, J.; Sakmár, M.; Vlk, M.; İlem-Özdemir, D.; Ekinci, M.; Srinivasan, S.; Rajabzadeh, A.R.; Ricci-Junior, E.; Alencar, L.M.R.; Al Qahtani, M.; Santos-Oliveira, R. Radiolabeled nanomaterials for biomedical applications: Radiopharmacy in the era of nanotechnology. EJNMMI Radiopharm. Chem., 2022, 7(1), 8.
[http://dx.doi.org/10.1186/s41181-022-00161-4] [PMID: 35467307]
[2]
Kim, D.; Shin, K.; Kwon, S.G.; Hyeon, T. Synthesis and biomedical applications of multifunctional nanoparticles. Adv. Mater., 2018, 30(49), 1802309.
[http://dx.doi.org/10.1002/adma.201802309] [PMID: 30133009]
[3]
Yan, Q.; Dong, H.; Su, J.; Han, J.; Song, B.; Wei, Q.; Shi, Y. A review of 3D printing technology for medical applications. Engineering, 2018, 4(5), 729-742.
[http://dx.doi.org/10.1016/j.eng.2018.07.021]
[4]
Li, J.; Liu, Y.; Ren, J.; Tay, B.Z.; Luo, T.; Fan, L.; Sun, D.; Luo, G.; Lau, D.; Marcos; Lam, R.H.W. Antibody-coated microstructures for selective isolation of immune cells in blood. Lab Chip, 2020, 20(6), 1072-1082.
[http://dx.doi.org/10.1039/D0LC00078G] [PMID: 32100806]
[5]
Lau, D.; Jian, W.; Yu, Z.; Hui, D. Nano-engineering of construction materials using molecular dynamics simulations: Prospects and challenges. Compos., Part B Eng., 2018, 143, 282-291.
[http://dx.doi.org/10.1016/j.compositesb.2018.01.014]
[6]
Sanchez, F.; Sobolev, K. Nanotechnology in concrete – A review. Constr. Build. Mater., 2010, 24(11), 2060-2071.
[http://dx.doi.org/10.1016/j.conbuildmat.2010.03.014]
[7]
Karplus, M.; McCammon, J.A. Molecular dynamics simulations of biomolecules. Nat. Struct. Biol., 2002, 9(9), 646-652.
[http://dx.doi.org/10.1038/nsb0902-646] [PMID: 12198485]
[8]
Buehler, M.J. Ed.; Atomistic modeling of materials failure; Springer US: Boston, MA, 2008.
[http://dx.doi.org/10.1007/978-0-387-76426-9]
[9]
Ramakrishna, S.; Mayer, J.; Wintermantel, E.; Leong, K.W. Biomedical applications of polymer-composite materials: A review. Compos. Sci. Technol., 2001, 61(9), 1189-1224.
[http://dx.doi.org/10.1016/S0266-3538(00)00241-4]
[10]
Jian, W.; Hui, D.; Lau, D. Nanoengineering in biomedicine: Current development and future perspectives. Nanotechnol. Rev., 2020, 9(1), 700-715.
[http://dx.doi.org/10.1515/ntrev-2020-0053]
[11]
Tibbitt, M.W.; Rodell, C.B.; Burdick, J.A.; Anseth, K.S. Progress in material design for biomedical applications. Proc. Natl. Acad. Sci. USA, 2015, 112(47), 14444-14451.
[http://dx.doi.org/10.1073/pnas.1516247112] [PMID: 26598696]
[12]
Jain, P.K.; Huang, X.; El-Sayed, I.H.; El-Sayed, M.A. Noble metals on the nanoscale: Optical and photothermal properties and some applications in imaging, sensing, biology, and medicine. Acc. Chem. Res., 2008, 41(12), 1578-1586.
[http://dx.doi.org/10.1021/ar7002804] [PMID: 18447366]
[13]
Sharma, P. NiTi shape memory alloy: Physical and tribological characterization. J. Mech. Behav. Mater., 2018, 27(1-2), 20180009.
[http://dx.doi.org/10.1515/jmbm-2018-0009]
[14]
Mishin, Y.; Mehl, M.J.; Papaconstantopoulos, D.A. Embeddedatom potential for B 2 - NiAl. Phys. Rev. B Condens. Matter, 2002, 65(22), 224114.
[http://dx.doi.org/10.1103/PhysRevB.65.224114]
[15]
Mendelev, M.I.; Han, S.; Srolovitz, D.J.; Ackland, G.J.; Sun, D.Y.; Asta, M. Development of new interatomic potentials appropriate for crystalline and liquid iron. Philos. Mag., 2003, 83(35), 3977-3994.
[http://dx.doi.org/10.1080/14786430310001613264]
[16]
Williams, P.L.; Mishin, Y.; Hamilton, J.C. An embedded-atom potential for the Cu–Ag system. Model. Simul. Mater. Sci. Eng., 2006, 14(5), 817-833.
[http://dx.doi.org/10.1088/0965-0393/14/5/002]
[17]
Lee, B.J.; Baskes, M.I. Second nearestneighbor modified embedded-atom-method potential. Phys. Rev. B Condens. Matter, 2000, 62(13), 8564-8567.
[http://dx.doi.org/10.1103/PhysRevB.62.8564]
[18]
Lee, B.J.; Baskes, M.I.; Kim, H.; Koo Cho, Y. Second nearest-neighbor modified embedded atom method potentials for bcc transition metals. Phys. Rev. B Condens. Matter, 2001, 64(18), 184102.
[http://dx.doi.org/10.1103/PhysRevB.64.184102]
[19]
Jang, H.S.; Kim, K.M.; Lee, B.J. Modified embedded-atom method interatomic potentials for pure Zn and Mg-Zn binary system. Calphad, 2018, 60, 200-207.
[http://dx.doi.org/10.1016/j.calphad.2018.01.003]
[20]
Hao, H.; Lau, D. Atomistic modeling of metallic thin films by modified embedded atom method. Appl. Surf. Sci., 2017, 422, 1139-1146.
[http://dx.doi.org/10.1016/j.apsusc.2017.05.011]
[21]
Elkhateeb, M.G.; Shin, Y.C. Molecular dynamics-based cohesive zone representation of Ti6Al4V/TiC composite interface. Mater. Des., 2018, 155, 161-169.
[http://dx.doi.org/10.1016/j.matdes.2018.05.054]
[22]
Choi, W.M.; Jo, Y.H.; Sohn, S.S.; Lee, S.; Lee, B.J. Understanding the physical metallurgy of the CoCrFeMnNi high-entropy alloy: An atomistic simulation study. NPJ Computat. Mater., 2018, 4(1), 1.
[http://dx.doi.org/10.1038/s41524-017-0060-9]
[23]
Zepeda-Ruiz, L.A.; Stukowski, A.; Oppelstrup, T.; Bulatov, V.V. Probing the limits of metal plasticity with molecular dynamics simulations. Nature, 2017, 550(7677), 492-495.
[http://dx.doi.org/10.1038/nature23472] [PMID: 28953878]
[24]
Shibuta, Y.; Sakane, S.; Miyoshi, E.; Okita, S.; Takaki, T.; Ohno, M. Heterogeneity in homogeneous nucleation from billion-atom molecular dynamics simulation of solidification of pure metal. Nat. Commun., 2017, 8(1), 10.
[http://dx.doi.org/10.1038/s41467-017-00017-5] [PMID: 28381864]
[25]
Morrison, K.R.; Cherukara, M.J.; Kim, H.; Strachan, A. Role of grain size on the martensitic transformation and ultra-fast superelasticity in shape memory alloys. Acta Mater., 2015, 95, 37-43.
[http://dx.doi.org/10.1016/j.actamat.2015.05.015]
[26]
Hao, H.; Lau, D. Evolution of interfacial structure and stress induced by interfacial lattice mismatch in layered metallic nanocomposites. Adv. Theory Simul., 2018, 1(8), 1800047.
[http://dx.doi.org/10.1002/adts.201800047]
[27]
Sebeck, K.; Shao, C.; Kieffer, J. Alkane–metal interfacial structure and elastic properties by molecular dynamics simulation. ACS Appl. Mater. Interfaces, 2016, 8(26), 16885-16896.
[http://dx.doi.org/10.1021/acsami.6b01665] [PMID: 27282363]
[28]
Brandt, E.G.; Lyubartsev, A.P. Molecular dynamics simulations of adsorption of amino acid side chain analogues and a titanium binding peptide on the TiO2 (100) surface. J. Phys. Chem. C, 2015, 119(32), 18126-18139.
[http://dx.doi.org/10.1021/acs.jpcc.5b02670]
[29]
Li, S.; Liu, Y.; Zheng, Z.; Liu, X.; Huang, H.; Han, Z.; Ren, L. Biomimetic robust superhydrophobic stainless-steel surfaces with antimicrobial activity and molecular dynamics simulation. Chem. Eng. J., 2019, 372, 852-861.
[http://dx.doi.org/10.1016/j.cej.2019.04.200]
[30]
Muruve, N.G.G.; Cheng, Y.F.; Feng, Y.; Liu, T.; Muruve, D.A.; Hassett, D.J.; Irvin, R.T. Peptide-based biocoatings for corrosion protection of stainless steel biomaterial in a chloride solution. Mater. Sci. Eng. C, 2016, 68, 695-700.
[http://dx.doi.org/10.1016/j.msec.2016.06.053] [PMID: 27524070]
[31]
Chen, J.; Wang, J.; Zhu, W. Zinc ion-induced conformational changes in new Delphi metallo-β-lactamase 1 probed by molecular dynamics simulations and umbrella sampling. Phys. Chem. Chem. Phys., 2017, 19(4), 3067-3075.
[http://dx.doi.org/10.1039/C6CP08105C] [PMID: 28079218]
[32]
Savelyev, A.; MacKerell, A.D., Jr. Competition among Li(+), Na(+), K(+), and Rb(+) monovalent ions for DNA in molecular dynamics simulations using the additive CHARMM36 and Drude polarizable force fields. J. Phys. Chem. B, 2015, 119(12), 4428-4440.
[http://dx.doi.org/10.1021/acs.jpcb.5b00683] [PMID: 25751286]
[33]
Ni, J.; Ling, H.; Zhang, S.; Wang, Z.; Peng, Z.; Benyshek, C.; Zan, R.; Miri, A.K.; Li, Z.; Zhang, X.; Lee, J.; Lee, K.J.; Kim, H.J.; Tebon, P.; Hoffman, T.; Dokmeci, M.R.; Ashammakhi, N.; Li, X.; Khademhosseini, A. Three-dimensional printing of metals for biomedical applications. Mater. Today Bio, 2019, 3, 100024.
[http://dx.doi.org/10.1016/j.mtbio.2019.100024] [PMID: 32159151]
[34]
Bai, L.; Gong, C.; Chen, X.; Sun, Y.; Zhang, J.; Cai, L.; Zhu, S.; Xie, S.Q. Additive manufacturing of customized metallic orthopedic implants: Materials, structures, and surface modifications. Metals, 2019, 9(9), 1004.
[http://dx.doi.org/10.3390/met9091004]
[35]
MacKerell, AD., Jr; Banavali, N.; Foloppe, N. Development and current status of the CHARMM force field for nucleic acids. Biopolymers, 2000, 56(4), 257-265.
[http://dx.doi.org/10.1002/1097-0282(2000)56:4<257:AID-BIP10029>3.0.CO;2-W]
[36]
Shivakumar, D.; Williams, J.; Wu, Y.; Damm, W.; Shelley, J.; Sherman, W. Prediction of absolute solvation free energies using molecular dynamics free energy perturbation and the OPLS force field. J. Chem. Theory Comput., 2010, 6(5), 1509-1519.
[http://dx.doi.org/10.1021/ct900587b] [PMID: 26615687]
[37]
Wang, J.; Wolf, R.M.; Caldwell, J.W.; Kollman, P.A.; Case, D.A. Development and testing of a general amber force field. J. Comput. Chem., 2004, 25(9), 1157-1174.
[http://dx.doi.org/10.1002/jcc.20035] [PMID: 15116359]
[38]
Pérez, A.; Marchán, I.; Svozil, D.; Sponer, J.; Cheatham, T.E., III; Laughton, C.A.; Orozco, M. Refinement of the AMBER force field for nucleic acids: improving the description of α/γ conformers. Biophys. J., 2007, 92(11), 3817-3829.
[http://dx.doi.org/10.1529/biophysj.106.097782] [PMID: 17351000]
[39]
Oostenbrink, C.; Villa, A.; Mark, A.E.; Van Gunsteren, W.F. A biomolecular force field based on the free enthalpy of hydration and solvation: The GROMOS force-field parameter sets 53A5 and 53A6. J. Comput. Chem., 2004, 25(13), 1656-1676.
[http://dx.doi.org/10.1002/jcc.20090] [PMID: 15264259]
[40]
Zhang, C.; Lu, C.; Jing, Z.; Wu, C.; Piquemal, J.P.; Ponder, J.W.; Ren, P. AMOEBA polarizable atomic multipole force field for nucleic acids. J. Chem. Theory Comput., 2018, 14(4), 2084-2108.
[http://dx.doi.org/10.1021/acs.jctc.7b01169] [PMID: 29438622]
[41]
Cao, L.; Ren, H.; Miao, J.; Guo, W.; Li, Y.; Li, G. Validation of polarizable force field parameters for nucleic acids by intermolecular interactions. Front. Chem. Sci. Eng., 2016, 10(2), 203-212.
[http://dx.doi.org/10.1007/s11705-016-1572-4]
[42]
Henriques, J.; Cragnell, C.; Skepö, M. Molecular dynamics simulations of intrinsically disordered proteins: Force field evaluation and comparison with experiment. J. Chem. Theory Comput., 2015, 11(7), 3420-3431.
[http://dx.doi.org/10.1021/ct501178z] [PMID: 26575776]
[43]
Horta, B.A.C.; Merz, P.T.; Fuchs, P.F.J.; Dolenc, J.; Riniker, S.; Hünenberger, P.H. A GROMOS-compatible force field for small organic molecules in the condensed phase: The 2016H66 parameter set. J. Chem. Theory Comput., 2016, 12(8), 3825-3850.
[http://dx.doi.org/10.1021/acs.jctc.6b00187] [PMID: 27248705]
[44]
Aytenfisu, A.H.; Spasic, A.; Grossfield, A.; Stern, H.A.; Mathews, D.H. Revised RNA dihedral parameters for the amber force field improve RNA molecular dynamics. J. Chem. Theory Comput., 2017, 13(2), 900-915.
[http://dx.doi.org/10.1021/acs.jctc.6b00870] [PMID: 28048939]
[45]
Tarakanova, A.; Huang, W.; Qin, Z.; Kaplan, D.L.; Buehler, M.J. Modeling and experiment reveal structure and nanomechanics across the inverse temperature transition in B. mori silk-elastin-like protein polymers. ACS Biomater. Sci. Eng., 2017, 3(11), 2889-2899.
[http://dx.doi.org/10.1021/acsbiomaterials.6b00688] [PMID: 33418710]
[46]
Tarakanova, A.; Yeo, G.C.; Baldock, C.; Weiss, A.S.; Buehler, M.J. Tropoelastin is a flexible molecule that retains its canonical shape. Macromol. Biosci., 2019, 19(3), 1800250.
[http://dx.doi.org/10.1002/mabi.201800250] [PMID: 30369047]
[47]
Tarakanova, A.; Buehler, M.J. Molecular modeling of protein materials: case study of elastin. Model. Simul. Mater. Sci. Eng., 2013, 21(6), 063001.
[http://dx.doi.org/10.1088/0965-0393/21/6/063001]
[48]
Tarakanova, A.; Yeo, G.C.; Baldock, C.; Weiss, A.S.; Buehler, M.J. Molecular model of human tropoelastin and implications of associated mutations. Proc. Natl. Acad. Sci. USA, 2018, 115(28), 7338-7343.
[http://dx.doi.org/10.1073/pnas.1801205115] [PMID: 29946030]
[49]
Yeo, G.C.; Tarakanova, A.; Baldock, C.; Wise, S.G.; Buehler, M.J.; Weiss, A.S. Subtle balance of tropoelastin molecular shape and flexibility regulates dynamics and hierarchical assembly. Sci. Adv., 2016, 2(2), e1501145.
[http://dx.doi.org/10.1126/sciadv.1501145] [PMID: 26998516]
[50]
Li, N.; Jang, H.; Yuan, M.; Li, W.; Yun, X.; Lee, J.; Du, Q.; Nussinov, R.; Hou, J.; Lal, R.; Zhang, F. Graphite-templated amyloid nanostructures formed by a potential pentapeptide inhibitor for alzheimer’s disease: A combined study of real-time atomic force microscopy and molecular dynamics simulations. Langmuir, 2017, 33(27), 6647-6656.
[http://dx.doi.org/10.1021/acs.langmuir.7b00414] [PMID: 28605901]
[51]
Laghaei, R.; Evans, D.G.; Coalson, R.D. Metal binding sites of human H-chain ferritin and iron transport mechanism to the ferroxidase sites: A molecular dynamics simulation study. Proteins, 2013, 81(6), 1042-1050.
[http://dx.doi.org/10.1002/prot.24251] [PMID: 23344859]
[52]
Yu, Z.; Lau, D. Molecular dynamics study on stiffness and ductility in chitin–protein composite. J. Mater. Sci., 2015, 50(21), 7149-7157.
[http://dx.doi.org/10.1007/s10853-015-9271-y]
[53]
Yu, Z.; Xu, Z.; Lau, D. Effect of acidity on chitin–protein interface: A molecular dynamics study. Bionanoscience, 2014, 4(3), 207-215.
[http://dx.doi.org/10.1007/s12668-014-0138-5]
[54]
Wang, Y.; Qin, Z.; Buehler, M.J.; Xu, Z. Intercalated water layers promote thermal dissipation at bio–nano interfaces. Nat. Commun., 2016, 7(1), 12854.
[http://dx.doi.org/10.1038/ncomms12854] [PMID: 27659484]
[55]
Chin, S.L.; Lu, Q.; Dane, E.L.; Dominguez, L.; McKnight, C.J.; Straub, J.E.; Grinstaff, M.W. Combined molecular dynamics simulations and experimental studies of the structure and dynamics of poly-amido-saccharides. J. Am. Chem. Soc., 2016, 138(20), 6532-6540.
[http://dx.doi.org/10.1021/jacs.6b01837] [PMID: 27119983]
[56]
Kmiecik, S.; Gront, D.; Kolinski, M.; Wieteska, L.; Dawid, A.E.; Kolinski, A. Coarse-grained protein models and their applications. Chem. Rev., 2016, 116(14), 7898-7936.
[http://dx.doi.org/10.1021/acs.chemrev.6b00163] [PMID: 27333362]
[57]
Yeo, J.; Jung, G.; Tarakanova, A.; Martín-Martínez, F.J.; Qin, Z.; Cheng, Y.; Zhang, Y.W.; Buehler, M.J. Multiscale modeling of keratin, collagen, elastin and related human diseases: Perspectives from atomistic to coarse-grained molecular dynamics simulations. Extreme Mech. Lett., 2018, 20, 112-124.
[http://dx.doi.org/10.1016/j.eml.2018.01.009] [PMID: 33344740]
[58]
Yu, Z.; Lau, D. Development of a coarse-grained α-chitin model on the basis of MARTINI forcefield. J. Mol. Model., 2015, 21(5), 128.
[http://dx.doi.org/10.1007/s00894-015-2670-9] [PMID: 25914123]
[59]
Uusitalo, J.J.; Ingólfsson, H.I.; Akhshi, P.; Tieleman, D.P.; Marrink, S.J. Martini coarsegrained force field: Extension to DNA. J. Chem. Theory Comput., 2015, 11(8), 3932-3945.
[http://dx.doi.org/10.1021/acs.jctc.5b00286] [PMID: 26574472]
[60]
Tarakanova, A.; Ozsvar, J.; Weiss, A.S.; Buehler, M.J. Coarse-grained model of tropoelastin self-assembly into nascent fibrils. Mater. Today Bio, 2019, 3, 100016.
[http://dx.doi.org/10.1016/j.mtbio.2019.100016] [PMID: 32159149]
[61]
Orekhov, P.S.; Kholina, E.G.; Bozdaganyan, M.E.; Nesterenko, A.M.; Kovalenko, I.B.; Strakhovskaya, M.G. Molecular mechanism of uptake of cationic photoantimicrobial phthalocyanine across bacterial membranes revealed by molecular dynamics simulations. J. Phys. Chem. B, 2018, 122(14), 3711-3722.
[http://dx.doi.org/10.1021/acs.jpcb.7b11707] [PMID: 29553736]
[62]
Deng, S.; Gao, E.; Wang, Y.; Sen, S.; Sreenivasan, S.T.; Behura, S.; Král, P.; Xu, Z.; Berry, V. Confined, oriented, and electrically anisotropic graphene wrinkles on bacteria. ACS Nano, 2016, 10(9), 8403-8412.
[http://dx.doi.org/10.1021/acsnano.6b03214] [PMID: 27391776]
[63]
Plattner, N.; Doerr, S.; De Fabritiis, G.; Noé, F. Complete protein–protein association kinetics in atomic detail revealed by molecular dynamics simulations and Markov modelling. Nat. Chem., 2017, 9(10), 1005-1011.
[http://dx.doi.org/10.1038/nchem.2785] [PMID: 28937668]
[64]
Yoon, B.J. Hidden Markov models and their applications in biological sequence analysis. Curr. Genomics, 2009, 10(6), 402-415.
[http://dx.doi.org/10.2174/138920209789177575] [PMID: 20190955]
[65]
Nitta, S.; Numata, K. Biopolymer-based nanoparticles for drug/gene delivery and tissue engineering. Int. J. Mol. Sci., 2013, 14(1), 1629-1654.
[http://dx.doi.org/10.3390/ijms14011629] [PMID: 23344060]
[66]
Herdiana, Y.; Wathoni, N.; Shamsuddin, S.; Joni, I.M.; Muchtaridi, M. Chitosan-based nanoparticles of targeted drug delivery system in breast cancer treatment. Polymers, 2021, 13(11), 1717.
[http://dx.doi.org/10.3390/polym13111717] [PMID: 34074020]
[67]
Makvandi, P.; Iftekhar, S.; Pizzetti, F.; Zarepour, A.; Zare, E.N.; Ashrafizadeh, M.; Agarwal, T.; Padil, V.V.T.; Mohammadinejad, R.; Sillanpaa, M.; Maiti, T.K.; Perale, G.; Zarrabi, A.; Rossi, F. Functionalization of polymers and nanomaterials for water treatment, food packaging, textile and biomedical applications: A review. Environ. Chem. Lett., 2021, 19(1), 583-611.
[http://dx.doi.org/10.1007/s10311-020-01089-4]
[68]
Díez-Pascual, A.M. Surface engineering of nanomaterials with polymers, biomolecules, and small ligands for nanomedicine. Materials, 2022, 15(9), 3251.
[http://dx.doi.org/10.3390/ma15093251] [PMID: 35591584]
[69]
Sun, H. COMPASS: An ab initio force-field optimized for condensed-phase applications overview with details on alkane and benzene compounds. J. Phys. Chem. B, 1998, 102(38), 7338-7364.
[http://dx.doi.org/10.1021/jp980939v]
[70]
Tam, L.; Lau, D. A molecular dynamics investigation on the crosslinking and physical properties of epoxy-based materials. RSC Advances, 2014, 4(62), 33074-33081.
[http://dx.doi.org/10.1039/C4RA04298K]
[71]
van Duin, A.C.T.; Dasgupta, S.; Lorant, F.; Goddard, W.A. ReaxFF: A reactive force field for hydrocarbons. J. Phys. Chem. A, 2001, 105(41), 9396-9409.
[http://dx.doi.org/10.1021/jp004368u]
[72]
Odegard, G.M.; Jensen, B.D.; Gowtham, S.; Wu, J.; He, J.; Zhang, Z. Predicting mechanical response of crosslinked epoxy using ReaxFF. Chem. Phys. Lett., 2014, 591, 175-178.
[http://dx.doi.org/10.1016/j.cplett.2013.11.036]
[73]
Vashisth, A.; Ashraf, C.; Bakis, C.E.; van Duin, A.C.T. Effect of chemical structure on thermo-mechanical properties of epoxy polymers: Comparison of accelerated ReaxFF simulations and experiments. Polymer, 2018, 158, 354-363.
[http://dx.doi.org/10.1016/j.polymer.2018.11.005]
[74]
Chowdhury, S.C.; Elder, R.M.; Sirk, T.W.; Gillespie, J.W., Jr Epoxy resin thermo-mechanics and failure modes: Effects of cure and cross-linker length. Compos., Part B Eng., 2020, 186, 107814.
[http://dx.doi.org/10.1016/j.compositesb.2020.107814]
[75]
Hao, H.; Chow, C.L.; Lau, D. Carbon monoxide release mechanism in cellulose combustion using reactive forcefield. Fuel, 2020, 269, 117422.
[http://dx.doi.org/10.1016/j.fuel.2020.117422]
[76]
Nazarychev, V.M.; Lyulin, A.V.; Larin, S.V.; Gurtovenko, A.A.; Kenny, J.M.; Lyulin, S.V. Molecular dynamics simulations of uniaxial deformation of thermoplastic polyimides. Soft Matter, 2016, 12(17), 3972-3981.
[http://dx.doi.org/10.1039/C6SM00230G] [PMID: 27033967]
[77]
Wang, X.; Jian, W.; Lu, H.; Lau, D.; Fu, Y.Q. Modeling strategy for enhanced recovery strength and a tailorable shape transition behavior in shape memory copolymers. Macromolecules, 2019, 52(16), 6045-6054.
[http://dx.doi.org/10.1021/acs.macromol.9b00992]
[78]
Lei, M.; Chen, Z.; Lu, H.; Yu, K. Recent progress in shape memory polymer composites: methods, properties, applications and prospects. Nanotechnol. Rev., 2019, 8(1), 327-351.
[http://dx.doi.org/10.1515/ntrev-2019-0031]
[79]
Bobby, S.; Samad, MA Epoxy composites in biomedical engineering. In: Materials for Biomedical Engineering; Elsevier, 2019; pp. 145-174.
[http://dx.doi.org/10.1016/B978-0-12-816874-5.00005-0]
[80]
Sresht, V.; Pádua, A.A.H.; Blankschtein, D. Liquid-phase exfoliation of phosphorene: design rules from molecular dynamics simulations. ACS Nano, 2015, 9(8), 8255-8268.
[http://dx.doi.org/10.1021/acsnano.5b02683] [PMID: 26192620]
[81]
Zhou, A.; Büyüköztürk, O.; Lau, D. Debonding of concrete-epoxy interface under the coupled effect of moisture and sustained load. Cement Concr. Compos., 2017, 80, 287-297.
[http://dx.doi.org/10.1016/j.cemconcomp.2017.03.019]
[82]
Zhou, A.; Qiu, Q.; Chow, C.L.; Lau, D. Interfacial performance of aramid, basalt and carbon fiber reinforced polymer bonded concrete exposed to high temperature. Compos., Part A Appl. Sci. Manuf., 2020, 131, 105802.
[http://dx.doi.org/10.1016/j.compositesa.2020.105802]
[83]
Lau, D.; Broderick, K.; Buehler, M.J.; Büyüköztürk, O. A robust nanoscale experimental quantification of fracture energy in a bilayer material system. Proc. Natl. Acad. Sci. USA, 2014, 111(33), 11990-11995.
[http://dx.doi.org/10.1073/pnas.1402893111] [PMID: 25097263]
[84]
Mielke, S.L.; Belytschko, T.; Schatz, G.C. Nanoscale fracture mechanics. Annu. Rev. Phys. Chem., 2007, 58(1), 185-209.
[http://dx.doi.org/10.1146/annurev.physchem.58.032806.104502] [PMID: 17059367]
[85]
Zhou, A.; Tam, L.; Yu, Z.; Lau, D. Effect of moisture on the mechanical properties of CFRP–wood composite: An experimental and atomistic investigation. Compos., Part B Eng., 2015, 71, 63-73.
[http://dx.doi.org/10.1016/j.compositesb.2014.10.051]
[86]
Tam, L.; Zhou, A.; Yu, Z.; Qiu, Q.; Lau, D. Understanding the effect of temperature on the interfacial behavior of CFRP-wood composite via molecular dynamics simulations. Compos., Part B Eng., 2017, 109, 227-237.
[http://dx.doi.org/10.1016/j.compositesb.2016.10.030]
[87]
Jian, W.; Tam, L.; Lau, D. Atomistic study of interfacial creep behavior in epoxy-silica bilayer system. Compos., Part B Eng., 2018, 132, 229-236.
[http://dx.doi.org/10.1016/j.compositesb.2017.09.006]
[88]
Begines, B.; Ortiz, T.; Pérez-Aranda, M.; Martínez, G.; Merinero, M.; Argüelles-Arias, F.; Alcudia, A. Polymeric nanoparticles for drug delivery: Recent developments and future prospects. Nanomaterials, 2020, 10(7), 1403.
[http://dx.doi.org/10.3390/nano10071403] [PMID: 32707641]
[89]
Makadia, H.K.; Siegel, S.J. Poly lactic-co-glycolic acid (PLGA) as biodegradable controlled drug delivery carrier. Polymers, 2011, 3(3), 1377-1397.
[http://dx.doi.org/10.3390/polym3031377] [PMID: 22577513]
[90]
Spychalska, K.; Zając, D.; Baluta, S.; Halicka, K.; Cabaj, J. Functional polymers structures for (Bio) sensing application—A review. Polymers, 2020, 12(5), 1154.
[http://dx.doi.org/10.3390/polym12051154] [PMID: 32443618]
[91]
Klabukov, I.; Balyasin, M.; Krasilnikova, O.; Tenchurin, T.; Titov, A.; Krasheninnikov, M.; Mudryak, D.; Sulina, Y.; Shepelev, A.; Chvalun, S.; Dyuzheva, T.; Yakimova, A.; Sosin, D.; Lyundup, A.; Baranovskii, D.; Shegay, P.; Kaprin, A. Angiogenic modification of microfibrous polycaprolactone by pcmv-vegf165 plasmid promotes local vascular growth after implantation in rats. Int. J. Mol. Sci., 2023, 24(2), 1399.
[http://dx.doi.org/10.3390/ijms24021399] [PMID: 36674913]
[92]
Shetty, K.; Bhandari, A.; Yadav, K.S. Nanoparticles incorporated in nanofibers using electrospinning: A novel nano-in-nano delivery system. J. Control. Release, 2022, 350, 421-434.
[http://dx.doi.org/10.1016/j.jconrel.2022.08.035] [PMID: 36002053]
[93]
Liu, W.; Luo, X.; Bao, Y.; Liu, Y.P.; Ning, G.H.; Abdelwahab, I.; Li, L.; Nai, C.T.; Hu, Z.G.; Zhao, D.; Liu, B.; Quek, S.Y.; Loh, K.P. A two-dimensional conjugated aromatic polymer via C–C coupling reaction. Nat. Chem., 2017, 9(6), 563-570.
[http://dx.doi.org/10.1038/nchem.2696] [PMID: 28537590]
[94]
Lee, B.; Lee, K.; Panda, S.; Gonzales-Rojas, R.; Chong, A.; Bugay, V.; Park, H.M.; Brenner, R.; Murthy, N.; Lee, H.Y. Nanoparticle delivery of CRISPR into the brain rescues a mouse model of fragile X syndrome from exaggerated repetitive behaviours. Nat. Biomed. Eng., 2018, 2(7), 497-507.
[http://dx.doi.org/10.1038/s41551-018-0252-8] [PMID: 30948824]
[95]
Hussain, S.; Joo, J.; Kang, J.; Kim, B.; Braun, G.B.; She, Z.G.; Kim, D.; Mann, A.P.; Mölder, T.; Teesalu, T.; Carnazza, S.; Guglielmino, S.; Sailor, M.J.; Ruoslahti, E. Antibiotic-loaded nanoparticles targeted to the site of infection enhance antibacterial efficacy. Nat. Biomed. Eng., 2018, 2(2), 95-103.
[http://dx.doi.org/10.1038/s41551-017-0187-5] [PMID: 29955439]
[96]
Padmanabhan, P.; Kumar, A.; Kumar, S.; Chaudhary, R.K.; Gulyás, B. Nanoparticles in practice for molecular-imaging applications: An overview. Acta Biomater., 2016, 41, 1-16.
[http://dx.doi.org/10.1016/j.actbio.2016.06.003] [PMID: 27265153]
[97]
Ban, I.; Stergar, J.; Maver, U. NiCu magnetic nanoparticles: Review of synthesis methods, surface functionalization approaches, and biomedical applications. Nanotechnol. Rev., 2018, 7(2), 187-207.
[http://dx.doi.org/10.1515/ntrev-2017-0193]
[98]
Yildiz, I. Applications of magnetic nanoparticles in biomedical separation and purification. Nanotechnol. Rev., 2016, 5(3), 331-340.
[http://dx.doi.org/10.1515/ntrev-2015-0012]
[99]
Wen, Y.H.; Huang, R.; Shao, G.F.; Sun, S.G. Thermal stability of Co–Pt and Co–Au core–shell structured nanoparticles: insights from molecular dynamics simulations. J. Phys. Chem. Lett., 2017, 8(17), 4273-4278.
[http://dx.doi.org/10.1021/acs.jpclett.7b01880] [PMID: 28837772]
[100]
Sridhar, D.B.; Gupta, R.; Rai, B. Effect of surface coverage and chemistry on self-assembly of monolayer protected gold nanoparticles: a molecular dynamics simulation study. Phys. Chem. Chem. Phys., 2018, 20(40), 25883-25891.
[http://dx.doi.org/10.1039/C8CP04044C] [PMID: 30288520]
[101]
Issa, I.; Amodeo, J.; Réthoré, J.; Joly-Pottuz, L.; Esnouf, C.; Morthomas, J.; Perez, M.; Chevalier, J.; Masenelli-Varlot, K. In situ investigation of MgO nanocube deformation at room temperature. Acta Mater., 2015, 86, 295-304.
[http://dx.doi.org/10.1016/j.actamat.2014.12.001]
[102]
Meena, S.K.; Sulpizi, M. From gold nanoseeds to nanorods: The microscopic origin of the anisotropic growth. Angew. Chem. Int. Ed., 2016, 55(39), 11960-11964.
[http://dx.doi.org/10.1002/anie.201604594] [PMID: 27560039]
[103]
Atilhan, M.; Aparicio, S. Molecular dynamics simulations of metal nanoparticles in deep eutectic solvents. J. Phys. Chem. C, 2018, 122(31), 18029-18039.
[http://dx.doi.org/10.1021/acs.jpcc.8b02582]
[104]
Salorinne, K.; Malola, S.; Wong, O.A.; Rithner, C.D.; Chen, X.; Ackerson, C.J.; Häkkinen, H. Conformation and dynamics of the ligand shell of a water-soluble Au102 nanoparticle. Nat. Commun., 2016, 7(1), 10401.
[http://dx.doi.org/10.1038/ncomms10401] [PMID: 26791253]
[105]
Xie, B.; Buehler, M.J.; Xu, Z. Directed self-assembly of endfunctionalized nanofibers: From percolated networks to liquid crystal-like phases. Nanotechnology, 2015, 26(20), 205602.
[http://dx.doi.org/10.1088/0957-4484/26/20/205602] [PMID: 25913165]
[106]
Elsabahy, M.; Wooley, K.L. Design of polymeric nanoparticles for biomedical delivery applications. Chem. Soc. Rev., 2012, 41(7), 2545-2561.
[http://dx.doi.org/10.1039/c2cs15327k] [PMID: 22334259]
[107]
Kaufman, J.J.; Ottman, R.; Tao, G.; Shabahang, S.; Banaei, E.H.; Liang, X.; Johnson, S.G.; Fink, Y.; Chakrabarti, R.; Abouraddy, A.F. In-fiber production of polymeric particles for biosensing and encapsulation. Proc. Natl. Acad. Sci. USA, 2013, 110(39), 15549-15554.
[http://dx.doi.org/10.1073/pnas.1310214110] [PMID: 24019468]
[108]
Zhang, C.; Liu, T.; Wang, W.; Bell, C.A.; Han, Y.; Fu, C.; Peng, H.; Tan, X.; Král, P.; Gaus, K.; Gooding, J.J.; Whittaker, A.K. Tuning of the aggregation behavior of fluorinated polymeric nanoparticles for improved therapeutic efficacy. ACS Nano, 2020, 14(6), 7425-7434.
[http://dx.doi.org/10.1021/acsnano.0c02954] [PMID: 32401485]
[109]
Meneksedag-Erol, D.; Tang, T.; Uludağ, H. Mechanistic insights into the role of glycosaminoglycans in delivery of polymeric nucleic acid nanoparticles by molecular dynamics simulations. Biomaterials, 2018, 156, 107-120.
[http://dx.doi.org/10.1016/j.biomaterials.2017.11.037] [PMID: 29195180]
[110]
Yang, G.; Gong, H.; Liu, T.; Sun, X.; Cheng, L.; Liu, Z. Twodimensional magnetic WS2@Fe3O4 nanocomposite with mesoporous silica coating for drug delivery and imaging-guided therapy of cancer. Biomaterials, 2015, 60, 62-71.
[http://dx.doi.org/10.1016/j.biomaterials.2015.04.053] [PMID: 25985153]
[111]
Zhang, H.; Wu, H.; Wang, J.; Yang, Y.; Wu, D.; Zhang, Y.; Zhang, Y.; Zhou, Z.; Yang, S. Graphene oxide-BaGdF5 nanocomposites for multi-modal imaging and photothermal therapy. Biomaterials, 2015, 42, 66-77.
[http://dx.doi.org/10.1016/j.biomaterials.2014.11.055] [PMID: 25542794]
[112]
Li, Y.; Wang, S.; Wang, Q. Enhancement of tribological properties of polymer composites reinforced by functionalized graphene. Compos., Part B Eng., 2017, 120, 83-91.
[http://dx.doi.org/10.1016/j.compositesb.2017.03.063]
[113]
Li, Y.; Wang, S.; Wang, Q.; Xing, M. A comparison study on mechanical properties of polymer composites reinforced by carbon nanotubes and graphene sheet. Compos., Part B Eng., 2018, 133, 35-41.
[http://dx.doi.org/10.1016/j.compositesb.2017.09.024]
[114]
Li, Y.; Wang, S.; Wang, Q.; Xing, M. Enhancement of fracture properties of polymer composites reinforced by carbon nanotubes: A molecular dynamics study. Carbon, 2018, 129, 504-509.
[http://dx.doi.org/10.1016/j.carbon.2017.12.029]
[115]
Gopalakrishnan, R.; Azhagiya Singam, E.R.; Vijaya Sundar, J.; Subramanian, V. Interaction of collagen like peptides with gold nanosurfaces: A molecular dynamics investigation. Phys. Chem. Chem. Phys., 2015, 17(7), 5172-5186.
[http://dx.doi.org/10.1039/C4CP04969A] [PMID: 25600994]
[116]
Albanese, A.; Tang, P.S.; Chan, W.C.W. The effect of nanoparticle size, shape, and surface chemistry on biological systems. Annu. Rev. Biomed. Eng., 2012, 14(1), 1-16.
[http://dx.doi.org/10.1146/annurev-bioeng-071811-150124] [PMID: 22524388]
[117]
Jian, W.; Lau, D. Creep performance of CNT-based nanocomposites: A parametric study. Carbon, 2019, 153, 745-756.
[http://dx.doi.org/10.1016/j.carbon.2019.07.069]
[118]
Jian, W.; Lau, D. Understanding the effect of functionalization in CNT-epoxy nanocomposite from molecular level. Compos. Sci. Technol., 2020, 191, 108076.
[http://dx.doi.org/10.1016/j.compscitech.2020.108076]
[119]
Xia, W.; Qin, X.; Zhang, Y.; Sinko, R.; Keten, S. Achieving enhanced interfacial adhesion and dispersion in cellulose nanocomposites via amorphous interfaces. Macromolecules, 2018, 51(24), 10304-10311.
[http://dx.doi.org/10.1021/acs.macromol.8b02243]
[120]
Sun, Q.; Meng, Z.; Zhou, G.; Lin, S.P.; Kang, H.; Keten, S.; Guo, H.; Su, X. Multi-scale computational analysis of unidirectional carbon fiber reinforced polymer composites under various loading conditions. Compos. Struct., 2018, 196, 30-43.
[http://dx.doi.org/10.1016/j.compstruct.2018.05.025]
[121]
McKinlay, A.C.; Morris, R.E.; Horcajada, P.; Férey, G.; Gref, R.; Couvreur, P.; Serre, C. BioMOFs: Metal-organic frameworks for biological and medical applications. Angew. Chem. Int. Ed., 2010, 49(36), 6260-6266.
[http://dx.doi.org/10.1002/anie.201000048] [PMID: 20652915]
[122]
Wang, L.; Zheng, M.; Xie, Z. Nanoscale metal–organic frameworks for drug delivery: A conventional platform with new promise. J. Mater. Chem. B Mater. Biol. Med., 2018, 6(5), 707-717.
[http://dx.doi.org/10.1039/C7TB02970E] [PMID: 32254257]
[123]
Heinen, J.; Ready, A.D.; Bennett, T.D.; Dubbeldam, D.; Friddle, R.W.; Burtch, N.C. Elucidating the variable-temperature mechanical properties of a negative thermal expansion metal–organic framework. ACS Appl. Mater. Interfaces, 2018, 10(25), 21079-21083.
[http://dx.doi.org/10.1021/acsami.8b06604] [PMID: 29873475]
[124]
Erucar, I.; Keskin, S. Efficient storage of drug and cosmetic molecules in biocompatible metal organic frameworks: A molecular simulation study. Ind. Eng. Chem. Res., 2016, 55(7), 1929-1939.
[http://dx.doi.org/10.1021/acs.iecr.5b04556]
[125]
Skoulidas, A.I. Molecular dynamics simulations of gas diffusion in metal-organic frameworks: Argon in CuBTC. J. Am. Chem. Soc., 2004, 126(5), 1356-1357.
[http://dx.doi.org/10.1021/ja039215+] [PMID: 14759190]
[126]
Watanabe, T.; Sholl, D.S. Accelerating applications of metalorganic frameworks for gas adsorption and separation by computational screening of materials. Langmuir, 2012, 28(40), 14114-14128.
[http://dx.doi.org/10.1021/la301915s] [PMID: 22783907]
[127]
Kotzabasaki, M.; Galdadas, I.; Tylianakis, E.; Klontzas, E.; Cournia, Z.; Froudakis, G.E. Multiscale simulations reveal IRMOF-74-III as a potent drug carrier for gemcitabine delivery. J. Mater. Chem. B Mater. Biol. Med., 2017, 5(18), 3277-3282.
[http://dx.doi.org/10.1039/C7TB00220C] [PMID: 32264393]
[128]
Su, J.; Yuan, S.; Wang, H.Y.; Huang, L.; Ge, J.Y.; Joseph, E.; Qin, J.; Cagin, T.; Zuo, J.L.; Zhou, H.C. Redox-switchable breathing behavior in tetrathiafulvalene-based metal–organic frameworks. Nat. Commun., 2017, 8(1), 2008.
[http://dx.doi.org/10.1038/s41467-017-02256-y] [PMID: 29222485]
[129]
Gaillac, R.; Pullumbi, P.; Beyer, K.A.; Chapman, K.W.; Keen, D.A.; Bennett, T.D.; Coudert, F.X. Liquid metal–organic frameworks. Nat. Mater., 2017, 16(11), 1149-1154.
[http://dx.doi.org/10.1038/nmat4998] [PMID: 29035353]
[130]
Ghalei, B.; Sakurai, K.; Kinoshita, Y.; Wakimoto, K.; Isfahani, A.P.; Song, Q.; Doitomi, K.; Furukawa, S.; Hirao, H.; Kusuda, H.; Kitagawa, S.; Sivaniah, E. Enhanced selectivity in mixed matrix membranes for CO2 capture through efficient dispersion of aminefunctionalized MOF nanoparticles. Nat. Energy, 2017, 2(7), 17086.
[http://dx.doi.org/10.1038/nenergy.2017.86]
[131]
Zhang, H.; Hou, J.; Hu, Y.; Wang, P.; Ou, R.; Jiang, L.; Liu, J.Z.; Freeman, B.D.; Hill, A.J.; Wang, H. Ultrafast selective transport of alkali metal ions in metal organic frameworks with subnanometer pores. Sci. Adv., 2018, 4(2), eaaq0066.
[http://dx.doi.org/10.1126/sciadv.aaq0066] [PMID: 29487910]
[132]
Semino, R.; Moreton, J.C.; Ramsahye, N.A.; Cohen, S.M.; Maurin, G. Understanding the origins of metal–organic framework/polymer compatibility. Chem. Sci., 2018, 9(2), 315-324.
[http://dx.doi.org/10.1039/C7SC04152G] [PMID: 29629100]
[133]
Habibzadeh, F.; Sadraei, S.M.; Mansoori, R.; Singh Chauhan, N.P.; Sargazi, G. Nanomaterials supported by polymers for tissue engineering applications: A review. Heliyon, 2022, 8(12), e12193.
[http://dx.doi.org/10.1016/j.heliyon.2022.e12193] [PMID: 36578390]
[134]
Silva, M.; Alves, N.M.; Paiva, M.C. Graphene-polymer nanocomposites for biomedical applications. Polym. Adv. Technol., 2018, 29(2), 687-700.
[http://dx.doi.org/10.1002/pat.4164]
[135]
Costanzo, H.; Gooch, J.; Frascione, N. Nanomaterials for optical biosensors in forensic analysis. Talanta, 2023, 253, 123945.
[http://dx.doi.org/10.1016/j.talanta.2022.123945] [PMID: 36191514]
[136]
Jackson, N.E.; Webb, M.A.; de Pablo, J.J. Recent advances in machine learning towards multiscale soft materials design. Curr. Opin. Chem. Eng., 2019, 23, 106-114.
[http://dx.doi.org/10.1016/j.coche.2019.03.005]
[137]
Vyatskikh, A.; Delalande, S.; Kudo, A.; Zhang, X.; Portela, C.M.; Greer, J.R. Additive manufacturing of 3D nano-architected metals. Nat. Commun., 2018, 9(1), 593.
[http://dx.doi.org/10.1038/s41467-018-03071-9] [PMID: 29426947]
[138]
Zong, H.; Pilania, G.; Ding, X.; Ackland, GJ.; Lookman, T. Developing an interatomic potential for martensitic phase transformations in zirconium by machine learning. NPJ Computational Materials., 2018, 4(1), 48.
[139]
Li, Y.; Li, H.; Pickard, F.C., IV; Narayanan, B.; Sen, F.G.; Chan, M.K.Y.; Sankaranarayanan, S.K.R.S.; Brooks, B.R.; Roux, B. Machine learning force field parameters from ab initio data. J. Chem. Theory Comput., 2017, 13(9), 4492-4503.
[http://dx.doi.org/10.1021/acs.jctc.7b00521] [PMID: 28800233]
[140]
Chmiela, S.; Tkatchenko, A.; Sauceda, H.E.; Poltavsky, I.; Schütt, K.T.; Müller, K.R. Machine learning of accurate energy-conserving molecular force fields. Sci. Adv., 2017, 3(5), e1603015.
[http://dx.doi.org/10.1126/sciadv.1603015] [PMID: 28508076]
[141]
Chmiela, S.; Sauceda, H.E.; Müller, K.R.; Tkatchenko, A. Towards exact molecular dynamics simulations with machine-learned force fields. Nat. Commun., 2018, 9(1), 3887.
[http://dx.doi.org/10.1038/s41467-018-06169-2] [PMID: 30250077]
[142]
Huan, T.D.; Batra, R.; Chapman, J.; Krishnan, S.; Chen, L.; Ramprasad, R. A universal strategy for the creation of machine learning-based atomistic force fields. NPJ Computational Materials, 2017, 3(1), 37.
[http://dx.doi.org/10.1038/s41524-017-0042-y]
[143]
Kruglov, I.; Sergeev, O.; Yanilkin, A.; Oganov, A.R. Energy-free machine learning force field for aluminum. Sci. Rep., 2017, 7(1), 8512.
[http://dx.doi.org/10.1038/s41598-017-08455-3] [PMID: 28819297]
[144]
Singh, S.K.; Bejagam, K.K.; An, Y.; Deshmukh, S.A. Machinelearning based stacked ensemble model for accurate analysis of molecular dynamics simulations. J. Phys. Chem. A, 2019, 123(24), 5190-5198.
[http://dx.doi.org/10.1021/acs.jpca.9b03420] [PMID: 31150239]
[145]
Chan, H.; Narayanan, B.; Cherukara, M.J.; Sen, F.G.; Sasikumar, K.; Gray, S.K.; Chan, M.K.Y.; Sankaranarayanan, S.K.R.S. Machine learning classical interatomic potentials for molecular dynamics from first-principles training data. J. Phys. Chem. C, 2019, 123(12), 6941-6957.
[http://dx.doi.org/10.1021/acs.jpcc.8b09917]
[146]
Wang, J.; Olsson, S.; Wehmeyer, C.; Pérez, A.; Charron, N.E.; de Fabritiis, G.; Noé, F.; Clementi, C. Machine learning of coarsegrained molecular dynamics force fields. ACS Cent. Sci., 2019, 5(5), 755-767.
[http://dx.doi.org/10.1021/acscentsci.8b00913] [PMID: 31139712]
[147]
Duan, K.; He, Y.; Li, Y.; Liu, J.; Zhang, J.; Hu, Y.; Lin, R.; Wang, X.; Deng, W.; Li, L. Machine-learning assisted coarse-grained model for epoxies over wide ranges of temperatures and cross-linking degrees. Mater. Des., 2019, 183, 108130.
[http://dx.doi.org/10.1016/j.matdes.2019.108130]
[148]
Gastegger, M.; Behler, J.; Marquetand, P. Machine learning molecular dynamics for the simulation of infrared spectra. Chem. Sci., 2017, 8(10), 6924-6935.
[http://dx.doi.org/10.1039/C7SC02267K] [PMID: 29147518]
[149]
Mittal, S.; Shukla, D. Recruiting machine learning methods for molecular simulations of proteins. Mol. Simul., 2018, 44(11), 891-904.
[http://dx.doi.org/10.1080/08927022.2018.1448976]
[150]
Noé, F.; De Fabritiis, G.; Clementi, C. Machine learning for protein folding and dynamics. Curr. Opin. Struct. Biol., 2020, 60, 77-84.
[http://dx.doi.org/10.1016/j.sbi.2019.12.005] [PMID: 31881449]
[151]
Ryckbosch, S.M.; Wender, P.A.; Pande, V.S. Molecular dynamics simulations reveal ligand-controlled positioning of a peripheral protein complex in membranes. Nat. Commun., 2017, 8(1), 6.
[http://dx.doi.org/10.1038/s41467-016-0015-8] [PMID: 28232750]
[152]
Cooper, S.; Khatib, F.; Treuille, A.; Barbero, J.; Lee, J.; Beenen, M.; Leaver-Fay, A.; Baker, D.; Popović, Z.; players, F. Predicting protein structures with a multiplayer online game. Nature, 2010, 466(7307), 756-760.
[http://dx.doi.org/10.1038/nature09304] [PMID: 20686574]
[153]
Khatib, F.; Cooper, S.; Tyka, M.D.; Xu, K.; Makedon, I.; Popović, Z.; Baker, D.; players, F. Algorithm discovery by protein folding game players. Proc. Natl. Acad. Sci. USA, 2011, 108(47), 18949-18953.
[http://dx.doi.org/10.1073/pnas.1115898108] [PMID: 22065763]
[154]
Cipcigan, F.; Carrieri, A.P.; Pyzer-Knapp, E.O.; Krishna, R.; Hsiao, Y.W.; Winn, M.; Ryadnov, M.G.; Edge, C.; Martyna, G.; Crain, J. Accelerating molecular discovery through data and physical sciences: Applications to peptide-membrane interactions. J. Chem. Phys., 2018, 148(24), 241744.
[http://dx.doi.org/10.1063/1.5027261] [PMID: 29960328]
[155]
Tarakanova, A.; Huang, W.; Weiss, A.S.; Kaplan, D.L.; Buehler, M.J. Computational smart polymer design based on elastin protein mutability. Biomaterials, 2017, 127, 49-60.
[http://dx.doi.org/10.1016/j.biomaterials.2017.01.041] [PMID: 28279921]
[156]
Yu, C.H.; Qin, Z.; Martin-Martinez, F.J.; Buehler, M.J. A selfconsistent sonification method to translate amino acid sequences into musical compositions and application in protein design using artificial intelligence. ACS Nano, 2019, 13(7), 7471-7482.
[http://dx.doi.org/10.1021/acsnano.9b02180] [PMID: 31240912]
[157]
Yu, C.H.; Buehler, M.J. Sonification based de novo protein design using artificial intelligence, structure prediction, and analysis using molecular modeling. APL Bioeng., 2020, 4(1), 016108.
[http://dx.doi.org/10.1063/1.5133026] [PMID: 32206742]
[158]
Qin, Z.; Yu, Q.; Buehler, M.J. Machine learning model for fast prediction of the natural frequencies of protein molecules. RSC Advances, 2020, 10(28), 16607-16615.
[http://dx.doi.org/10.1039/C9RA04186A] [PMID: 35498827]
[159]
Qin, Z.; Wu, L.; Sun, H.; Huo, S.; Ma, T.; Lim, E.; Chen, P.Y.; Marelli, B.; Buehler, M.J. Artificial intelligence method to design and fold alpha-helical structural proteins from the primary amino acid sequence. Extreme Mech. Lett., 2020, 36, 100652.
[http://dx.doi.org/10.1016/j.eml.2020.100652]
[160]
Gu, G.X.; Chen, C.T.; Richmond, D.J.; Buehler, M.J. Bioinspired hierarchical composite design using machine learning: simulation, additive manufacturing, and experiment. Mater. Horiz., 2018, 5(5), 939-945.
[http://dx.doi.org/10.1039/C8MH00653A]
[161]
Chen, C.T.; Gu, G.X. Generative deep neural networks for inverse materials design using backpropagation and active learning. Adv. Sci., 2020, 7(5), 1902607.
[http://dx.doi.org/10.1002/advs.201902607] [PMID: 32154072]
[162]
Hathout, R.M.; Metwally, A.A. Towards better modelling of drugloading in solid lipid nanoparticles: Molecular dynamics, docking experiments and Gaussian Processes machine learning. Eur. J. Pharm. Biopharm., 2016, 108, 262-268.
[http://dx.doi.org/10.1016/j.ejpb.2016.07.019] [PMID: 27449631]
[163]
Yu, C.H.; Qin, Z.; Buehler, M.J. Artificial intelligence design algorithm for nanocomposites optimized for shear crack resistance. Nano Futures, 2019, 3(3), 035001.
[http://dx.doi.org/10.1088/2399-1984/ab36f0]
[164]
Fahimipour, F.; Dashtimoghadam, E.; Mahdi Hasani-Sadrabadi, M.; Vargas, J.; Vashaee, D.; Lobner, D.C.; Jafarzadeh Kashi, T.S.; Ghasemzadeh, B.; Tayebi, L. Enhancing cell seeding and osteogenesis of MSCs on 3D printed scaffolds through injectable BMP2 immobilized ECM-Mimetic gel. Dent. Mater., 2019, 35(7), 990-1006.
[http://dx.doi.org/10.1016/j.dental.2019.04.004] [PMID: 31027908]
[165]
Sultan, S.; Siqueira, G.; Zimmermann, T.; Mathew, A.P. 3D printing of nano-cellulosic biomaterials for medical applications. Curr. Opin. Biomed. Eng., 2017, 2, 29-34.
[http://dx.doi.org/10.1016/j.cobme.2017.06.002]
[166]
Chinga-Carrasco, G. Potential and limitations of nanocelluloses as components in biocomposite inks for three-dimensional bioprinting and for biomedical devices. Biomacromolecules, 2018, 19(3), 701-711.
[http://dx.doi.org/10.1021/acs.biomac.8b00053] [PMID: 29489338]
[167]
Kuzmenko, V.; Karabulut, E.; Pernevik, E.; Enoksson, P.; Gatenholm, P. Tailor-made conductive inks from cellulose nanofibrils for 3D printing of neural guidelines. Carbohydr. Polym., 2018, 189, 22-30.
[http://dx.doi.org/10.1016/j.carbpol.2018.01.097] [PMID: 29580403]
[168]
Hadiyat, M.A.; Wahyudi, R.D.; Sari, Y.; Herowati, E. Quality and reliability engineering in service industry: A proposed alternative improvement framework. InIOP Conference Series: Materials Science and Engineering, IOP Publishing., 2019, 528(1), p. 012076.
[http://dx.doi.org/10.1088/1757-899X/528/1/012076]
[169]
Klabukov, I.; Tenchurin, T.; Shepelev, A.; Baranovskii, D.; Mamagulashvili, V.; Dyuzheva, T.; Krasilnikova, O.; Balyasin, M.; Lyundup, A.; Krasheninnikov, M.; Sulina, Y.; Gomzyak, V.; Krasheninnikov, S.; Buzin, A.; Zayratyants, G.; Yakimova, A.; Demchenko, A.; Ivanov, S.; Shegay, P.; Kaprin, A.; Chvalun, S. Biomechanical behaviors and degradation properties of multilayered polymer scaffolds: The phase space method for bile duct design and bioengineering. Biomedicines, 2023, 11(3), 745.
[http://dx.doi.org/10.3390/biomedicines11030745] [PMID: 36979723]
[170]
Jeng, S.L.; Lu, J.C.; Wang, K. A review of reliability research on nanotechnology. IEEE Trans. Reliab., 2007, 56(3), 401-410.
[http://dx.doi.org/10.1109/TR.2007.903188]
[171]
Chandran, R. Finite element analysis in nanotechnology research. In: In: Finite Element Methods and Their Applications; IntechOpen, 2020.
[172]
Schmidt, J.; Marques, MR; Botti, S; Marques, MA Recent advances and applications of machine learning in solid-state materials science. NPJ Computational Materials, 2019, 5(1), 43.
[173]
Butler, K.T.; Davies, D.W.; Cartwright, H.; Isayev, O.; Walsh, A. Machine learning for molecular and materials science. Nature, 2018, 559(7715), 547-555.
[http://dx.doi.org/10.1038/s41586-018-0337-2] [PMID: 30046072]
[174]
Ray, P.C.; Yu, H.; Fu, P.P. Toxicity and environmental risks of nanomaterials: Challenges and future needs. J. Environ. Sci. Health Part C Environ. Carcinog. Ecotoxicol. Rev., 2009, 27(1), 1-35.
[http://dx.doi.org/10.1080/10590500802708267] [PMID: 19204862]
[175]
Dhawan, A.; Sharma, V. Toxicity assessment of nanomaterials: Methods and challenges. Anal. Bioanal. Chem., 2010, 398(2), 589-605.
[http://dx.doi.org/10.1007/s00216-010-3996-x] [PMID: 20652549]
[176]
Yadav, S.K.; Khan, Z.A.; Mishra, B.; Bahadur, S.; Kumar, A.; Yadav, B. The toxic side of nanotechnology: An insight into hazards to health and the ecosystem. Micro Nanosyst., 2022, 14(1), 21-33.
[http://dx.doi.org/10.2174/1876402913666210412160329]
[177]
Stankovich, M.; Behrens, E.; Burchell, J. Toward meaningful transparency and accountability of ai algorithms in public service delivery; , 2023. Available from: https://www.dai.com/uploads/ai-in-public-service.pdf
[178]
Passi, S.; Vorvoreanu, M. Overreliance on AI literature review. Microsoft Research; , 2022. Available from: https://www.microsoft.com/en-us/research/uploads/prod/2022/06/A ether-Overreliance-on-AI-Review-Final-6.21.22.pdf
[179]
Robust denaturation of villin headpiece by MoS2 nanosheet: Potential molecular origin of the nanotoxicity. Sci. Rep., 2016, 6(1), 1-8.
[PMID: 28442746]
[180]
Mukhopadhyay, T.K.; Bhattacharyya, K.; Datta, A. Gauging the nanotoxicity of h2D-C2N toward single-stranded DNA: An in silico molecular simulation approach. ACS Appl. Mater. Interfaces, 2018, 10(16), 13805-13818.
[http://dx.doi.org/10.1021/acsami.8b00494] [PMID: 29611415]
[181]
Ashton, S.; Song, Y.H.; Nolan, J.; Cadogan, E.; Murray, J.; Odedra, R.; Foster, J.; Hall, P.A.; Low, S.; Taylor, P.; Ellston, R.; Polanska, U.M.; Wilson, J.; Howes, C.; Smith, A.; Goodwin, R.J.A.; Swales, J.G.; Strittmatter, N.; Takáts, Z.; Nilsson, A.; Andren, P.; Trueman, D.; Walker, M.; Reimer, C.L.; Troiano, G.; Parsons, D.; De Witt, D.; Ashford, M.; Hrkach, J.; Zale, S.; Jewsbury, P.J.; Barry, S.T. Aurora kinase inhibitor nanoparticles target tumors with favorable therapeutic index in vivo. Sci. Transl. Med., 2016, 8(325), 325ra17.
[http://dx.doi.org/10.1126/scitranslmed.aad2355] [PMID: 26865565]
[182]
Bahadur, S.; Jha, M.K. Emerging nanoformulations for drug targeting to brain through intranasal delivery: A comprehensive review. J. Drug Deliv. Sci. Technol., 2022, 78, 103932.
[http://dx.doi.org/10.1016/j.jddst.2022.103932]
[183]
Bahadur, S.; Sachan, N.; Harwansh, R.K.; Deshmukh, R. Nanoparticlized system: Promising approach for the management of Alzheimer’s disease through intranasal delivery. Curr. Pharm. Des., 2020, 26(12), 1331-1344.
[http://dx.doi.org/10.2174/1381612826666200311131658] [PMID: 32160843]

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