Artificial Intelligence in Cerebellum Activation

Year : 2024 | Volume :01 | Issue : 01 | Page : 14-26
By

A. Mohamed Sikkander

Rajeev Ranjan

Sangeeta R Mishra

  1. Associate Professor and Head Department of Chemistry, Velammal Engineering College Chennai India
  2. Assistant Professor Department of Chemistry, DSPM University, Ranchi Jharkhand India
  3. Associate professor Department of Electronics & Telecommunication, Thakur College of Eng., Mumbai Maharashtra India

Abstract

Neuroscience plays a significant function during the progression of artificial intelligence. It provided inspiration for the development of human-like AI. There are two ways that neuroscience encourages us to develop AI systems. Neural networks that replicate human cognition and those that match the structure of the brain are the two objectives. Neural networks, which draw inspiration from the architecture of the human brain, are the engine behind contemporary artificial intelligence systems. Creating and using algorithms that mimic the neural systems present in the human brain is the aim of contemporary AI research. The cerebellum’s short- and long-term motor memory formation, the learning of gains in ocular reflex, and the timing of eye blink conditioning are all explained by the liquid-state machine (LSM) model. It integrates the Golgi cells—granule cells—random inhibitory recurrent neural network to a basic perception. The LSM model now incorporates the cerebellar internal model, which supports cognitive function and voluntary movement control. The present state of artificial intelligence (AI) is called deep learning, and it is based on neural network models that began with simple observation. It is thought that the cerebellum is the source of modern artificial intelligence (AI), since the LSM model of the cerebellum represents the brain’s implementation of deep learning.

Keywords: Artificial Intelligence (AI), Little brain, Cortex, Brain function, Cognitive science, Motor learning

[This article belongs to International Journal of Cheminformatics(ijci)]

How to cite this article: A. Mohamed Sikkander, Rajeev Ranjan, Sangeeta R Mishra. Artificial Intelligence in Cerebellum Activation. International Journal of Cheminformatics. 2024; 01(01):14-26.
How to cite this URL: A. Mohamed Sikkander, Rajeev Ranjan, Sangeeta R Mishra. Artificial Intelligence in Cerebellum Activation. International Journal of Cheminformatics. 2024; 01(01):14-26. Available from: https://journals.stmjournals.com/ijci/article=2024/view=143947




References

1. Ackerman S. Discovering the Brain. Washington (DC): National Academies Press (US); 1992. 2, Major Structures and Functions of the Brain. Available from: https://www.ncbi.nlm.nih.gov/books/NBK234157/
2. Jimsheleishvili S, Dididze M. Neuroanatomy, Cerebellum. [Updated 2023 Jul 24]. In: StatPearls [Internet]. Treasure Island (FL): StatPearls Publishing; 2023 Jan-.https://www.ncbi.nlm.nih.gov/books/NBK538167/
3. Klein AP, Ulmer JL, Quinet SA, Mathews V, Mark LP. Nonmotor Functions of the Cerebellum: An Introduction. AJNR Am J Neuroradiol. 2016 Jun;37(6):1005–9. doi: 10.3174/ajnr.A4720. Epub 2016 Mar 3. PMID: 26939633; PMCID: PMC7963530.
4. Thau L, Reddy V, Singh P. Anatomy, Central Nervous System. [Updated 2022 Oct 10]. In: StatPearls [Internet]. Treasure Island (FL): StatPearls Publishing; 2023 Jan-. Available from: https://www.ncbi.nlm.nih.gov/books/NBK542179/
5. Miller LE, Holdefer RN, Houk JC. The role of the cerebellum in modulating voluntary limb movement commands. Arch Ital Biol. 2002 Jul;140(3):175–83. PMID: 12173520.
6. Baud, R., Manzoori, A.R., Ijspeert, A. et al. Review of control strategies for lower-limb exoskeletons to assist gait. J NeuroEngineeringRehabil 18, 119 (2021). https://doi.org/10.1186/s12984–021–00906–3
7. Wilson, M., Cook, P.F. Rhythmic entrainment: Why humans want to, fireflies can’t help it, pet birds try, and sea lions have to be bribed. Psychon Bull Rev 23, 1647–1659 (2016). https://doi.org/10.3758/s13423–016–1013-x
8. Schwartz AB. Movement: How the Brain Communicates with the World. Cell. 2016 Mar 10;164(6):1122–1135. doi: 10.1016/j.cell.2016.02.038. PMID: 26967280; PMCID: PMC4818644.
9. Pierce JE, Péron J. The basal ganglia and the cerebellum in human emotion. SocCogn Affect Neurosci. 2020 Jul 1;15(5):599–613. doi: 10.1093/scan/nsaa076. PMID: 32507876; PMCID: PMC7328022.
10. Tyng CM, Amin HU, Saad MNM, Malik AS. The Influences of Emotion on Learning and Memory. Front Psychol. 2017 Aug 24;8:1454. doi: 10.3389/fpsyg.2017.01454. PMID: 28883804; PMCID: PMC5573739.
11. Mapelli, L.; Soda, T.; D’Angelo, E.; Prestori, F. The Cerebellar Involvement in Autism Spectrum Disorders: From the Social Brain to Mouse Models. Int. J. Mol. Sci. 2022, 23, 3894. https://doi.org/10.3390/ijms23073894
12. Manto M, Bower JM, Conforto AB, Delgado-García JM, da Guarda SN, Gerwig M, Habas C, Hagura N, Ivry RB, Mariën P, Molinari M, Naito E, Nowak DA, Oulad Ben Taib N, Pelisson D, Tesche CD, Tilikete C, Timmann D. Consensus paper: roles of the cerebellum in motor control–the diversity of ideas on cerebellar involvement in movement. Cerebellum. 2012 Jun;11(2):457–87. doi: 10.1007/s12311–011–0331–9. PMID: 22161499; PMCID: PMC4347949.
13. Baumann, O., Borra, R.J., Bower, J.M. et al. Consensus Paper: The Role of the Cerebellum in Perceptual Processes. Cerebellum 14, 197–220 (2015). https://doi.org/10.1007/s12311–014–0627–7
14. Prestori F, Mapelli L, D’Angelo E. Diverse Neuron Properties and Complex Network Dynamics in the Cerebellar Cortical Inhibitory Circuit. Front MolNeurosci. 2019 Nov 7;12:267. doi: 10.3389/fnmol.2019.00267. PMID: 31787879; PMCID: PMC6854908.
15. D’Angelo E, Antonietti A, Casali S, Casellato C, Garrido JA, Luque NR, Mapelli L, Masoli S, Pedrocchi A, Prestori F, Rizza MF, Ros E. Modeling the Cerebellar Microcircuit: New Strategies for a Long-Standing Issue. Front Cell Neurosci. 2016 Jul 8;10:176. doi: 10.3389/fncel.2016.00176. PMID: 27458345; PMCID: PMC4937064.
16. Negishi, Y., Kawai, Y. Geometric and functional architecture of visceral sensory microcircuitry. Brain StructFunct 216, 17–30 (2011). https://doi.org/10.1007/s00429–010–0294–5
17. Andrade-Talavera, Y., Fisahn, A. & Rodríguez-Moreno, A. Timing to be precise? An overview of spike timing-dependent plasticity, brain rhythmicity, and glial cells interplay within neuronal circuits. Mol Psychiatry 28, 2177–2188 (2023). https://doi.org/10.1038/s41380–023–02027-w
18. Gandolfi, D.; Bigiani, A.; Porro, C.A.; Mapelli, J. Inhibitory Plasticity: From Molecules to Computation and Beyond. Int. J. Mol. Sci. 2020, 21, 1805. https://doi.org/10.3390/ijms21051805
19. Speranza L, di Porzio U, Viggiano D, de Donato A, Volpicelli F. Dopamine: The Neuromodulator of Long-Term Synaptic Plasticity, Reward and Movement Control. Cells. 2021 Mar 26;10(4):735. doi: 10.3390/cells10040735. PMID: 33810328; PMCID: PMC8066851.
20. Appelbaum LG, Shenasa MA, Stolz L, Daskalakis Z. Synaptic plasticity and mental health: methods, challenges and opportunities. Neuropsychopharmacology. 2023 Jan;48(1):113–120. doi: 10.1038/s41386–022–01370-w. Epub 2022 Jul 9. PMID: 35810199; PMCID: PMC9700665.
21. Magor L. Lőrincz, Antoine R. Adamantidis,Monoaminergic control of brain states and sensory processing: Existing knowledge and recent insights obtained with optogenetics,Progress in Neurobiology,Volume 151,2017,Pages 237–253,ISSN 0301–0082, https://doi.org/10.1016/j.pneurobio.2016.09.003
22. Bayassi-Jakowicka, M.; Lietzau, G.; Czuba, E.; Steliga, A.; Waśkow, M.; Kowiański, P. Neuroplasticity and Multilevel System of Connections Determine the Integrative Role of Nucleus Accumbens in the Brain Reward System. Int. J. Mol. Sci. 2021, 22, 9806. https://doi.org/10.3390/ijms22189806
23. Wolfram Schultz,Getting Formal with Dopamine and Reward,Neuron,Volume 36, Issue 2,2002,Pages 241–263,ISSN 0896–6273, https://doi.org/10.1016/S0896–6273(02)00967–4
24. Jay A. Blundon, Ildar T. Bayazitov, Stanislav S. Zakharenko,Presynaptic Gating of Postsynaptically Expressed Plasticity at Mature ThalamocorticalSynapses,Journal of Neuroscience 2 November 2011, 31 (44) 16012–16025; DOI: 10.1523/JNEUROSCI.3281–11.2011
25. Corlett, P., Honey, G., Krystal, J. et al. Glutamatergic Model Psychoses: Prediction Error, Learning, and Inference. Neuropsychopharmacol 36, 294–315 (2011). https://doi.org/10.1038/npp.2010.163
26. Ong, WY.,Stohler, C.S. & Herr, D.R. Role of the Prefrontal Cortex in Pain Processing. MolNeurobiol 56, 1137–1166 (2019). https://doi.org/10.1007/s12035–018–1130–9
27. Lerner TN, Holloway AL, Seiler JL. Dopamine, Updated: Reward Prediction Error and Beyond. CurrOpinNeurobiol. 2021 Apr;67:123–130. doi: 10.1016/j.conb.2020.10.012. Epub 2020 Nov 14. PMID: 33197709; PMCID: PMC8116345.
28. Watabe–Uchida M, Eshel N, Uchida N. Neural Circuitry of Reward Prediction Error. Annu Rev Neurosci. 2017 Jul 25;40:373-394. doi: 10.1146/annurev-neuro-072116–031109. Epub 2017 Apr 24. PMID: 28441114; PMCID: PMC6721851.
29. Slater, C.; Liu, Y.; Weiss, E.; Yu, K.; Wang, Q. The Neuromodulatory Role of the Noradrenergic and Cholinergic Systems and Their Interplay in Cognitive Functions: A Focused Review. Brain Sci. 2022, 12, 890. https://doi.org/10.3390/brainsci12070890
30. Tanaka H, Ishikawa T, Lee J, Kakei S. The Cerebro-Cerebellum as a Locus of Forward Model: A Review. Front SystNeurosci. 2020 Apr 9;14:19. doi: 10.3389/fnsys.2020.00019. PMID: 32327978; PMCID: PMC7160920.
31. Takahiro Ishikawa, SaekaTomatsu, Jun Izawa, Shinji Kakei,Thecerebro-cerebellum: Could it be loci of forward models?,NeuroscienceResearch,Volume 104,2016,Pages 72–79,ISSN 0168–0102,https://doi.org/10.1016/j.neures.2015.12.003
32. Van Overwalle, F., Manto, M., Cattaneo, Z. et al. Consensus Paper: Cerebellum and Social Cognition. Cerebellum 19, 833–868 (2020). https://doi.org/10.1007/s12311–020–01155–1
33. Koh M, Markovich B. Neuroanatomy, Spinocerebellar Dorsal Tract. [Updated 2023 Aug 8]. In: StatPearls [Internet]. Treasure Island (FL): StatPearls Publishing; 2023 Jan-. Available from: https://www.ncbi.nlm.nih.gov/books/NBK556013/
34. Stecina K, Fedirchuk B, Hultborn H. Information to cerebellum on spinal motor networks mediated by the dorsal spinocerebellar tract. J Physiol. 2013 Nov 15;591(22):5433–43. doi: 10.1113/jphysiol.2012.249110. Epub 2013 Apr 22. PMID: 23613538; PMCID: PMC3853486.
35. Akay, T.; Murray, A.J. Relative Contribution of Proprioceptive and Vestibular Sensory Systems to Locomotion: Opportunities for Discovery in the Age of Molecular Science. Int. J. Mol. Sci. 2021, 22, 1467. https://doi.org/10.3390/ijms22031467
36. Bostan AC, Dum RP, Strick PL. Cerebellar networks with the cerebral cortex and basal ganglia. Trends Cogn Sci. 2013 May;17(5):241–54. doi: 10.1016/j.tics.2013.03.003. Epub 2013 Apr 9. PMID: 23579055; PMCID: PMC3645327.
37. Guangyu Robert Yang, Xiao-Jing Wang,Artificial Neural Networks for Neuroscientists: A Primer,Neuron,Volume 107, Issue 6,2020,Pages 1048–1070,ISSN 0896–6273,https://doi.org/10.1016/j.neuron.2020.09.005
38. Shao F, Shen Z. How can artificial neural networks approximate the brain? Front Psychol. 2023 Jan 9;13:970214. doi: 10.3389/fpsyg.2022.970214. PMID: 36698593; PMCID: PMC9868316.
39. Agatonovic-Kustrin S, Beresford R. Basic concepts of artificial neural network (ANN) modeling and its application in pharmaceutical research. J Pharm Biomed Anal. 2000 Jun;22(5):717–27. doi: 10.1016/s0731–7085(99)00272–1. PMID: 10815714.
40. Goel, A., Goel, A.K. & Kumar, A. The role of artificial neural network and machine learning in utilizing spatial information. Spat. Inf. Res. 31, 275–285 (2023). https://doi.org/10.1007/s41324–022–00494-x
41. MontesinosLópez OA, MontesinosLópez A, Crossa J. Multivariate Statistical Machine Learning Methods for Genomic Prediction [Internet]. Cham (CH): Springer; 2022. Chapter 10, Fundamentals of Artificial Neural Networks and Deep Learning. 2022 Jan 14. Available from: https://www.ncbi.nlm.nih.gov/books/NBK583971/ doi: 10.1007/978–3–030–89010-0_10
42. Furquim G, Filho GPR, Jalali R, Pessin G, Pazzi RW, Ueyama J. How to Improve Fault Tolerance in Disaster Predictions: A Case Study about Flash Floods Using IoT, ML and Real Data. Sensors (Basel). 2018 Mar 19;18(3):907. doi: 10.3390/s18030907. PMID: 29562657; PMCID: PMC5877203.
43. Gurney K. Neural networks for perceptual processing: from simulation tools to theories. Philos Trans R SocLond B Biol Sci. 2007 Mar 29;362(1479):339-53. doi: 10.1098/rstb.2006.1962. PMID: 17255023; PMCID: PMC2323553.
44. Deep Neural Networks: A New Framework for Modeling Biological Vision and Brain Information ProcessingNikolausKriegeskorteAnnual Review of Vision Science 2015 1:1, 417-446
45. Ma S, Liu J, Li W, Liu Y, Hui X, Qu P, Jiang Z, Li J, Wang J. Machine learning in TCM with natural products and molecules: current status and future perspectives. Chin Med. 2023 Apr 20;18(1):43. doi: 10.1186/s13020-023-00741-9. PMID: 37076902; PMCID: PMC10116715.
46. Choudhary, K., DeCost, B., Chen, C. et al. Recent advances and applications of deep learning methods in materials science. npjComput Mater 8, 59 (2022). https://doi.org/10.1038/s41524-022-00734-6
47. Xu, J.; Li, F.; Hou, M.; Wang, P. swAFL: A Library of High-Performance Activation Function for the Sunway Architecture. Electronics 2022, 11, 3141. https://doi.org/10.3390/electronics11193141
48. Mulindwa, D.B.; Du, S. An n-Sigmoid Activation Function to Improve the Squeeze-and-Excitation for 2D and 3D Deep Networks. Electronics 2023, 12, 911. https://doi.org/10.3390/electronics12040911
49. Shiv Ram Dubey, Satish Kumar Singh, BidyutBaranChaudhuri,Activation functions in deep learning: A comprehensive survey and benchmark,Neurocomputing,Volume 503,2022,Pages 92-108,ISSN 0925-2312, https://doi.org/10.1016/j.neucom.2022.06.111
50. IvánVallés-Pérez, Emilio Soria-Olivas, MarcelinoMartínez-Sober, Antonio J. Serrano-López, Joan Vila-Francés, Juan Gómez-Sanchís,Empirical study of the modulus as activation function in computer vision applications,Engineering Applications of Artificial Intelligence,Volume 120,2023,105863,ISSN 0952–1976, https://doi.org/10.1016/j.engappai.2023.105863
51. Boven E, Pemberton J, Chadderton P, Apps R, Costa RP. Cerebro-cerebellar networks facilitate learning through feedback decoupling. Nat Commun. 2023 Jan 4;14(1):51. doi: 10.1038/s41467–022–35658–8. PMID: 36599827; PMCID: PMC9813152.
52. Stanca, S.; Rossetti, M.; Bongioanni, P. The Cerebellum’s Role in Affective Disorders: The Onset of Its Social Dimension. Metabolites 2023, 13, 1113. https://doi.org/10.3390/metabo13111113
53. Manto M. Mechanisms of human cerebellar dysmetria: experimental evidence and current conceptual bases. J NeuroengRehabil. 2009 Apr 13;6:10. doi: 10.1186/1743-0003-6–10. PMID: 19364396; PMCID: PMC2679756.
54. Randy L. Buckner,The Cerebellum and Cognitive Function: 25 Years of Insight from Anatomy and Neuroimaging,Neuron,Volume 80, Issue 3,2013,Pages 807–815,ISSN 0896–6273, https://doi.org/10.1016/j.neuron.2013.10.044
55. Mario Manto, Mariano Serrao, Stefano Filippo Castiglia, Dagmar Timmann, ElinorTzvi-Minker, Ming-Kai Pan, Sheng-Han Kuo, Yoshikazu Ugawa,Neurophysiology of cerebellar ataxias and gait disorders,Clinical Neurophysiology Practice,Volume 8,2023,Pages 143–160,ISSN 2467–981X, https://doi.org/10.1016/j.cnp.2023.07.002
56. Cabaraux, P.; Gandini, J.; Kakei, S.; Manto, M.; Mitoma, H.; Tanaka, H. Dysmetria and Errors in Predictions: The Role of Internal Forward Model. Int. J. Mol. Sci. 2020, 21, 6900. https://doi.org/10.3390/ijms21186900
57. Zhang P, Duan L, Ou Y, Ling Q, Cao L, Qian H, Zhang J, Wang J, Yuan X. The cerebellum and cognitive neural networks. Front Hum Neurosci. 2023 Jul 28;17:1197459. doi: 10.3389/fnhum.2023.1197459. PMID: 37576472; PMCID: PMC10416251.
58. Ashida R., Cerminara N. L., Edwards R. J., Apps R., Brooks J. (2019). Sensorimotor, language, and working memory representation within the human cerebellum. Hum. Brain Mapp. 40 4732–4747. 10.1002/hbm.24733
59. Beuriat P. A., Cohen-Zimerman S., Smith G., Krueger F., Gordon B., Grafman J. (2020). A new insight on the role of the cerebellum for executive functions and emotion processing in adults. Front. Neurol. 11:593490. 10.3389/fneur.2020.593490
60. Craig B. T., Morrill A., Anderson B., Danckert J., Striemer C. L. (2021). Cerebellar lesions disrupt spatial and temporal visual attention. Cortex 139 27–42. 10.1016/j.cortex.2021.02.019
61. Erdal Y., Perk S., Keskinkılıc C., Bayramoglu B., Mahmutoglu A. S., Emre U. (2021). The assessment of cognitive functions in patients with isolated cerebellar infarctions: A follow-up study. Neurosci. Lett. 765:136252. 10.1016/j.neulet.2021.136252
62. Friedman, N.P., Robbins, T.W. The role of prefrontal cortex in cognitive control and executive function. Neuropsychopharmacol. 47, 72–89 (2022). https://doi.org/10.1038/s41386–021–01132-0
63. Xiaohan Lin, Xiaolong Zou, Zilong Ji, Tiejun Huang, Si Wu, YuanyuanMi,A brain-inspired computational model for spatio-temporal information processing,NeuralNetworks,Volume 143,2021,Pages 74-87,ISSN 0893-6080,https://doi.org/10.1016/j.neunet.2021.05.015
64. Gouhei Tanaka, Toshiyuki Yamane, Jean Benoit Héroux, RyoshoNakane, Naoki Kanazawa, Seiji Takeda, Hidetoshi Numata, Daiju Nakano, Akira Hirose,Recent advances in physical reservoir computing: A review,NeuralNetworks,Volume 115,2019,Pages 100–123,ISSN 0893–6080, https://doi.org/10.1016/j.neunet.2019.03.005
65. Cayco-Gajic NA, Silver RA. Re-evaluating Circuit Mechanisms Underlying Pattern Separation. Neuron. 2019 Feb 20;101(4):584–602. doi: 10.1016/j.neuron.2019.01.044. PMID: 30790539; PMCID: PMC7028396.
66. Ankri L, Husson Z, Pietrajtis K, Proville R, Léna C, Yarom Y, Dieudonné S, Uusisaari MY. A novel inhibitory nucleo-cortical circuit controls cerebellar Golgi cell activity. eLife. 2015;4:e06262.
67. Babadi B, Sompolinsky H. Sparseness and Expansion in Sensory Representations. Neuron. 2014;83:1213–1226.
68. Valle-Lisboa JC, Pomi A, Mizraji E. Multiplicative processing in the modeling of cognitive activities in large neural networks. Biophys Rev. 2023 Jun 22;15(4):767–785. doi: 10.1007/s12551–023-01074–5. PMID: 37681105; PMCID: PMC10480136.
69. Alammar J (2018) The Illustrated Transformer. https://jalammar.github.io/illustrated-transformer/
70. Ashida R., Cerminara N. L., Edwards R. J., Apps R., Brooks J. (2019). Sensorimotor, language, and working memory representation within the human cerebellum. Hum. Brain Mapp. 40 4732–4747. 10.1002/hbm.24733
71. Baumann O., Borra R. J., Bower J. M., Cullen K. E., Habas C., Ivry R. B., et al. (2015). Consensus paper: The role of the cerebellum in perceptual processes. Cerebellum 14 197–220. 10.1007/s12311–014–0627–7
72. Beuriat P. A., Cohen-Zimerman S., Smith G., Krueger F., Gordon B., Grafman J. (2020). A new insight on the role of the cerebellum for executive functions and emotion processing in adults. Front. Neurol. 11:593490. 10.3389/fneur.2020.593490
73. Brissenden J. A., Tobyne S. M., Halko M. A., Somers D. C. (2021). Stimulus-specific visual working memory representations in human cerebellar lobule VIIb/VIIIa. J. Neurosci. 41 1033–1045. 10.1523/JNEUROSCI.1253–20.2020
74. Castellazzi G., Bruno S. D., Toosy A. T., Casiraghi L., Palesi F., Savini G., et al. (2018). Prominent changes in cerebro-cerebellar functional connectivity during continuous cognitive processing. Front. Cell Neurosci. 12:331. 10.3389/fncel.2018.00331
75. Chang L., Soomro S. H., Zhang H., Fu H. (2021). Ankfy1 is involved in the maintenance of cerebellar purkinje cells. Front. Cell Neurosci. 15:648801. 10.3389/fncel.2021.648801
76. Cougnoux A., Yerger J. C., Fellmeth M., Serra-Vinardell J., Martin K., Navid F., et al. (2020). Single cell transcriptome analysis of Niemann-Pick disease, type C1 cerebella. Int. J. Mol. Sci. 21:5368. 10.3390/ijms21155368
77. Cristofori I., Cohen-Zimerman S., Grafman J. (2019). Executive functions. Handb. Clin. Neurol. 163 197–219. 10.1016/B978-0–12–804281–6.00011–2
78. Erdal Y., Perk S., Keskinkılıc C., Bayramoglu B., Mahmutoglu A. S., Emre U. (2021). The assessment of cognitive functions in patients with isolated cerebellar infarctions: A follow-up study. Neurosci. Lett. 765:136252. 10.1016/j.neulet.2021.136252
79. Moahamed Sikkander A, Manisankar P , Vedhi C Utilization of sodium montmorillonite clay for enhanced electrochemical sensing of amlodipine,Indian Journal of Chemistry-Section A(IJCA) 55 (5), 571–575DOI: 10.56042/ijca.v55i5.11669
80. Sivakumar ,R GopalakrishnanP, Abdul RazakMS,Comparative analysis of anti-reflection coatings on solar PV cells through TiO2 and SiO2 nanoparticles,Pigment & Resin Technology 51 (2), 171–177.https://doi.org/10.1108/PRT–08–2020–0084
81. Mohamed Sikkander A, Nasri NS., Review on Inorganic Nano crystals unique benchmark of Nanotechnology,Moroccan Journal of Chemistry 1 (2), 1–2 (2013) 47–54.https://doi.org/10.48317/IMIST.PRSM/morjchem-v1i2.1892
82. Mohamed Sikkander A., Bassyouni F, Yasmeen K, Mishra S.R, Lakshmi V.V. Synthesis of Zinc Oxide and Lead Nitrate Nanoparticles and their Applications: Comparative Studies of Bacterial and Fungal (E. coli, A. Niger). J. Appl. Organomet. Chem., 2023, 3(4), 255–267. https://doi.org/10.48309/JAOC.2023.415886.1115


Regular Issue Subscription Review Article
Volume 01
Issue 01
Received March 7, 2024
Accepted March 21, 2024
Published April 22, 2024