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Current Nanoscience

Editor-in-Chief

ISSN (Print): 1573-4137
ISSN (Online): 1875-6786

Mini-Review Article

Recent Trends in AFM Imaging Speed and Improvement Methods

Author(s): Ke Xu* and Bingge Wang

Volume 18, Issue 3, 2022

Published on: 15 July, 2021

Page: [277 - 290] Pages: 14

DOI: 10.2174/1573413717666210715110628

Price: $65

Abstract

Atomic Force Microscope (AFM) has become the main tool for observation and manipulation in nanotechnology research due to its nano-meter high resolution, but the slow imaging speed is one of the important reasons hindering the further development of AFM. This article first introduces the applications of AFM in cell biology in recent years, expresses the importance of rapid imaging in cell biology, and then summarizes the reasons affecting the imaging speed of AFM from three aspects: the limited bandwidth of system mechanical components, obvious inherent characteristics of piezoelectric scanners, and complex image processing algorithms. The improvement and optimization methods of mechanical parts or structure, control algorithm and image processing are reviewed for different influence reasons. Then, the advantages and of different improvement methods, as well as the improved imaging speed and imaging quality improvement effects, are compared. Imaging speed and resolution are both several to dozens of times higher than before, while ensuring image quality and without damaging the samples. The aim of this review is to enable students, the public and even experts of different knowledge backgrounds to learn directly, and select realizable improvement methods according to realistic conditions. Finally, the future development trend and further prospects of high-speed AFM are discussed.

Keywords: AFM, high imaging speed, cell biology, piezoelectric scanners, image reconstruction, molecular kinetics.

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[1]
Korayem, M.H.; Shahali, S.; Rastegar, Z.; Far, S.K. Path planning of the viscoelastic micro biological particle to minimize path length and particle’s deformation using genetic algorithm. Phys. Eng. Sci. Med., 2020, 43(3), 903-914.
[http://dx.doi.org/10.1007/s13246-020-00887-y] [PMID: 32607782]
[2]
Skamrahl, M.; Colin-York, H.; Barbieri, L.; Fritzsche, M. Simultaneous quantification of the interplay between molecular turnover and cell mechanics by AFM-FRAP. Small, 2019, 15(40), e1902202.
[http://dx.doi.org/10.1002/smll.201902202] [PMID: 31419037]
[3]
Li, M.; Xi, N.; Wang, Y.; Liu, L. In situ high-resolution afm imaging and force probing of cell culture medium-forming nanogranular surfaces for cell growth. IEEE Trans. Nanobioscience, 2020, 19(3), 385-393.
[http://dx.doi.org/10.1109/TNB.2020.2982164] [PMID: 32203024]
[4]
Ren, J.; Zou, Q. Adaptive-scanning, near-minimum-deformation atomic force microscope imaging of soft sample in liquid: Live mammalian cell example. Ultramicroscopy, 2018, 186, 150-157.
[http://dx.doi.org/10.1016/j.ultramic.2017.12.020] [PMID: 29335224]
[5]
Tian, Y.L.; Cai, K.H.; Zhang, D.W.; Liu, X.P.; Wang, F.J.; Shirinzade, B. Development of a XYZ scanner for home-made atomic force microscope based on FPAA control. Mech. Syst. Signal Process., 2019, 131, 222-242.
[http://dx.doi.org/10.1016/j.ymssp.2019.05.057]
[6]
Yeow, N.; Tabor, R.F.; Garnier, G. Atomic force microscopy: From red blood cells to immunohaematology. Adv. Colloid Interface Sci., 2017, 249, 149-162.
[http://dx.doi.org/10.1016/j.cis.2017.05.011] [PMID: 28515013]
[7]
Xu, K.; Huang, X.; Pan, Y.S. Recent development of high-speed atomic force microscopy in molecular biology. Micro & Nano Lett., 2020, 15, 354-358.
[http://dx.doi.org/10.1049/mnl.2019.0313]
[8]
Russell-Pavier, F.S.; Picco, L.; Day, J.C.; Shatil, N.R.; Yacoot, A.; Payton, O.D. High-speed contact mode atomic force microscopy with optical pickups. Meas. Sci. Technol., 2018, 29, 105902.
[http://dx.doi.org/10.1088/1361-6501/aad771]
[9]
Leitner, M.; Seferovic, H.; Stainer, S.; Buchroithner, B.; Schwalb, C.H.; Deutschinger, A.; Ebner, A. Atomic force microscopy imaging in turbid liquids: A promising tool in nanomedicine. Sensors (Basel), 2020, 20(13), 1-16.
[http://dx.doi.org/10.3390/s20133715] [PMID: 32630829]
[10]
Alunda, B.O.; Otieno, L.O.; Park, S.J.; Choi, S.G.; Kim, J.H.; Lee, Y.J. Development of a photo-thermal scan head for high-speed atomic force microscope. Meas. Sci. Technol., 2020, 31, 0477003.
[http://dx.doi.org/10.1088/1361-6501/ab5292]
[11]
Zou, T.; Hashiya, F.; Wei, Y.; Yu, Z.; Pandian, G.N.; Sugiyama, H. Direct observation of H3-H4 octasome by high-speed AFM. Chemistry, 2018, 24(60), 15998-16002.
[http://dx.doi.org/10.1002/chem.201804010] [PMID: 30088306]
[12]
Lim, K.; Kodera, N.; Wang, H.; Mohamed, M.S.; Hazawa, M.; Kobayashi, A.; Yoshida, T.; Hanayama, R.; Yano, S.; Ando, T.; Wong, R.W. High-speed afm reveals molecular dynamics of human influenza a hemagglutinin and its interaction with exosomes. Nano Lett., 2020, 20(9), 6320-6328.
[http://dx.doi.org/10.1021/acs.nanolett.0c01755] [PMID: 32787163]
[13]
Petit, C.; Kechiche, M.; Ivan, I.A.; Toscano, R.; Bolcato, V.; Planus, E.; Marchi, F. Visuo-haptic virtual exploration of single cell morphology and mechanics based on AFM mapping in fast mode. J. Micro-Bio Robotics, 2020, 16, 147-160.
[http://dx.doi.org/10.1007/s12213-020-00140-5]
[14]
Keyvani, A.; Alijani, F.; Sadeghian, H.; Maturova, K.; Goosen, H.; Van Keulen, F. Chaos: The speed limiting phenomenon in dynamic atomic force microscopy. J. Appl. Phys., 2017, 122, 224306.
[http://dx.doi.org/10.1063/1.5000130]
[15]
Dai, G.L.; Koenders, L.; Fluegge, J.; Hemmleb, M. Fast and accurate: High-speed metrological large-range AFM for surface and nanometrology. Meas. Sci. Technol., 2018, 29, 054012.
[http://dx.doi.org/10.1088/1361-6501/aaaf8a]
[16]
Ma, D.; Wang, R.; Chen, S.; Luo, T.; Chow, Y.T.; Sun, D. Microfluidic platform for probing cancer cells migration property under periodic mechanical confinement. Biomicrofluidics, 2018, 12(2), 024118.
[http://dx.doi.org/10.1063/1.5030135] [PMID: 29755635]
[17]
Lyubchenko, Y.L. Direct AFM visualization of the nano-scale dynamics of biomolecular complexes. J. Phys. D Appl. Phys., 2018, 51(40), 403001.
[http://dx.doi.org/10.1088/1361-6463/aad898] [PMID: 30410191]
[18]
Zuttion, F.; Redondo-Morata, L.; Marchesi, A.; Casuso, I. High-resolution and high-speed atomic force microscope imaging. Methods Mol. Biol., 2018, 1814, 181-200.
[http://dx.doi.org/10.1007/978-1-4939-8591-3_11] [PMID: 29956233]
[19]
Zuttion, F.; Sicard, D.; Dupin, L.; Vergoten, G.; Girard-Bock, C.; Madaoui, M.; Chevolot, Y.; Morvan, F.; Vidal, S.; Vasseur, J.J.; Souteyrand, E.; Phaner-Goutorbe, M. Deciphering multivalent glycocluster-lectin interactions through AFM characterization of the self-assembled nanostructures. Soft Matter, 2019, 15(36), 7211-7218.
[http://dx.doi.org/10.1039/C9SM00371A] [PMID: 31475271]
[20]
Zuttion, F.; Ligeour, C.; Vidal, O.; Wälte, M.; Morvan, F.; Vidal, S.; Vasseur, J.J.; Chevolot, Y.; Phaner-Goutorbe, M.; Schillers, H. The anti-adhesive effect of glycoclusters on Pseudomonas aeruginosa bacteria adhesion to epithelial cells studied by AFM single cell force spectroscopy. Nanoscale, 2018, 10(26), 12771-12778.
[http://dx.doi.org/10.1039/C8NR03285H] [PMID: 29946584]
[21]
Beck, M.; Hurt, E. The nuclear pore complex: Understanding its function through structural insight. Nat. Rev. Mol. Cell Biol., 2017, 18(2), 73-89.
[http://dx.doi.org/10.1038/nrm.2016.147] [PMID: 27999437]
[22]
Mohamed, M.S.; Kobayashi, A.; Taoka, A.; Watanabe-Nakayama, T.; Kikuchi, Y.; Hazawa, M.; Minamoto, T.; Fukumori, Y.; Kodera, N.; Uchihashi, T.; Ando, T.; Wong, R.W. High-speed atomic force microscopy reveals loss of nuclear pore resilience as a dying code in colorectal cancer cells. ACS Nano, 2017, 11(6), 5567-5578.
[http://dx.doi.org/10.1021/acsnano.7b00906] [PMID: 28530826]
[23]
Skotland, T.; Hessvik, N.P.; Sandvig, K.; Llorente, A. Exosomal lipid composition and the role of ether lipids and phosphoinositides in exosome biology. J. Lipid Res., 2019, 60(1), 9-18.
[http://dx.doi.org/10.1194/jlr.R084343] [PMID: 30076207]
[24]
Pan, Y.; Shlyakhtenko, L.S.; Lyubchenko, Y.L. Insight into dynamics of APOBEC3G protein in complexes with DNA assessed by high speed AFM. Nanoscale Adv., 2019, 1(10), 4016-4024.
[http://dx.doi.org/10.1039/C9NA00457B] [PMID: 33313478]
[25]
Gorle, S.; Pan, Y.; Sun, Z.; Shlyakhtenko, L.S.; Harris, R.S.; Lyubchenko, Y.L.; Vuković, L. Computational model and dynamics of monomeric full-length APOBEC3G. ACS Cent. Sci., 2017, 3(11), 1180-1188.
[http://dx.doi.org/10.1021/acscentsci.7b00346] [PMID: 29202020]
[26]
Pan, Y.; Sun, Z.; Maiti, A.; Kanai, T.; Matsuo, H.; Li, M.; Harris, R.S.; Shlyakhtenko, L.S.; Lyubchenko, Y.L. Nanoscale characterization of interaction of APOBEC3G with RNA. Biochemistry, 2017, 56(10), 1473-1481.
[http://dx.doi.org/10.1021/acs.biochem.6b01189] [PMID: 28029777]
[27]
Ueno, T.; Niwase, K.; Tsubokawa, D.; Kikuchi, K.; Takai, N.; Furuta, T.; Kawano, R.; Uchihashi, T. Dynamic behavior of an artificial protein needle contacting a membrane observed by high-speed atomic force microscopy. Nanoscale, 2020, 12(15), 8166-8173.
[http://dx.doi.org/10.1039/D0NR01121E] [PMID: 32239053]
[28]
Zhang, Y.; Hashemi, M.; Lv, Z.; Williams, B.; Popov, K.I.; Dokholyan, N.V.; Lyubchenko, Y.L. High-speed atomic force microscopy reveals structural dynamics of α-synuclein monomers and dimers. J. Chem. Phys., 2018, 148(12), 123322.
[http://dx.doi.org/10.1063/1.5008874] [PMID: 29604892]
[29]
Søe, K.; Delaissé, J.M. Time-lapse reveals that osteoclasts can move across the bone surface while resorbing. J. Cell Sci., 2017, 130(12), 2026-2035.
[PMID: 28473470]
[30]
Milberg, O.; Shitara, A.; Ebrahim, S.; Masedunskas, A.; Tora, M.; Tran, D.T.; Chen, Y.; Conti, M.A.; Adelstein, R.S.; Ten Hagen, K.G.; Weigert, R.; Roberto, W. Concerted actions of distinct nonmuscle myosin II isoforms drive intracellular membrane remodeling in live animals. J. Cell Biol., 2017, 216(7), 1925-1936.
[http://dx.doi.org/10.1083/jcb.201612126] [PMID: 28600434]
[31]
Takito, J.; Inoue, S.; Nakamura, M. The sealing zone in osteoclasts: a self-organized structure on the bone. Int. J. Mol. Sci., 2018, 19(4), 984.
[http://dx.doi.org/10.3390/ijms19040984] [PMID: 29587415]
[32]
Deguchi, T.; Fazeli, E.; Koho, S.; Pennanen, P.; Alanne, M.; Modi, M.; Eriksson, J.E.; Vienola, K.V.; Hänninen, P.E.; Peltonen, J.; Näreoja, T. Density and function of actin-microdomains in healthy and NF1 deficient osteoclasts revealed by the combined use of atomic force and stimulated emission depletion microscopy. J. Phys. D Appl. Phys., 2020, 53, 014003.
[http://dx.doi.org/10.1088/1361-6463/ab4838]
[33]
Aybeke, E.N. Belliot, G.; Lemaire-Ewing, S.; Estienney, M.; Lacroute, Y.; Pothier, P.; Bourillot, E.; Lesniewska, E. HS-AFM and SERS Analysis of Murine Norovirus Infection: Involvement of the Lipid Rafts. Small, 2017, 13, 1600918.
[http://dx.doi.org/10.1002/smll.201600918]
[34]
Shibata, T.; Furukawa, H.; Ito, Y.; Nagahama, M.; Hayashi, T.; Ishii-Teshima, M.; Nagai, M. Photocatalytic nanofabrication and intracellular raman imaging of living cells with functionalized AFM probes. Micromachines (Basel), 2020, 11(5), 495.
[http://dx.doi.org/10.3390/mi11050495] [PMID: 32414191]
[35]
Viji Babu, P.K.; Rianna, C.; Mirastschijski, U.; Radmacher, M. Nano-mechanical mapping of interdependent cell and ECM mechanics by AFM force spectroscopy. Sci. Rep., 2019, 9(1), 12317.
[http://dx.doi.org/10.1038/s41598-019-48566-7] [PMID: 31444369]
[36]
Hou, Y.; Wang, Z.B.; Li, D.Y.; Qiu, R.X.; Li, Y.; Jiang, J.L. Cellular shear adhesion force measurement and simultaneous imaging by atomic force microscope. J. Med. Biol. Eng., 2017, 37, 102-111.
[http://dx.doi.org/10.1007/s40846-016-0206-0]
[37]
Li, J.; Zheng, C.; Liu, B.; Chou, T.; Kim, Y.; Qiu, S.; Li, J.; Yan, W.; Fu, J. Controlled graphene encapsulation: A nanoscale shield for characterising single bacterial cells in liquid. Nanotechnology, 2018, 29(36), 365705.
[http://dx.doi.org/10.1088/1361-6528/aacba7] [PMID: 29889049]
[38]
Adams, J.D.; Erickson, B.W.; Grossenbacher, J.; Brugger, J.; Nievergelt, A.; Fantner, G.E. Harnessing the damping properties of materials for high-speed atomic force microscopy. Nat. Nanotechnol., 2016, 11(2), 147-151.
[http://dx.doi.org/10.1038/nnano.2015.254] [PMID: 26595334]
[39]
Liao, H.S.; Lei, K.K.; Tseng, Y. Fang High-speed force mapping based on an astigmatic atomic force microscope. Meas. Sci. Technol., 2019, 30, 027002.
[http://dx.doi.org/10.1088/1361-6501/aafa62]
[40]
Liao, H.S.; Wen, P.J.; Wu, L.G.; Jin, A.J. Effect of osmotic pressure on cellular stiffness as evaluated through force mapping measurements. J. Biomech. Eng., 2018, 140, 054502.
[http://dx.doi.org/10.1115/1.4039378]
[41]
Majstrzyk, W.; Ahmad, A.; Ivanov, T.; Reum, A.; Angelow, T.; Holz, M.; Gotszalk, T.; Rangelow, I.W. Thermomechanically and electromagnetically actuated piezoresistive cantilevers for fast-scanning probe microscopy investigations. Sens. Actuators, 2018, 276, 237-245.
[http://dx.doi.org/10.1016/j.sna.2018.04.028]
[42]
Cantrell, J.H.; Cantrell, S.A. Bifurcation, chaos, and scan instability in dynamic atomic force microscopy. J. Appl. Phys., 2016, 119, 125308.
[http://dx.doi.org/10.1063/1.4944714]
[43]
Sattelkow, J.; Fröch, J.E.; Winkler, R.; Hummel, S.; Schwalb, C.; Plank, H. Three-dimensional nanothermistors for thermal probing. ACS Appl. Mater. Interfaces, 2019, 11(25), 22655-22667.
[http://dx.doi.org/10.1021/acsami.9b04497] [PMID: 31154756]
[44]
Kumar, S.; Cartron, M.L.; Mullin, N.; Qian, P.; Leggett, G.J.; Hunter, C.N.; Hobbs, J.K. Direct imaging of protein organization in an intact bacterial organelle using high-resolution atomic force microscopy. ACS Nano, 2017, 11(1), 126-133.
[http://dx.doi.org/10.1021/acsnano.6b05647] [PMID: 28114766]
[45]
Schächtele, M.; Hänel, E.; Schäffer, T.E. Resonance compensating chirp mode for mapping the rheology of live cells by high-speed atomic force microscopy. Appl. Phys. Lett., 2018, 113, 093701.
[http://dx.doi.org/10.1063/1.5039911]
[46]
Zhang, Y.; Li, Y.; Shan, G.; Chen, Y.; Wang, Z.; Qian, J. Real-time scan speed control of the atomic force microscopy for reducing imaging time based on sample topography. Micron, 2018, 106, 1-6.
[http://dx.doi.org/10.1016/j.micron.2017.12.004] [PMID: 29278760]
[47]
Xie, S.W.; Ren, J. High-speed AFM imaging via iterative learning-based model predictive control. Mechatronics, 2019, 57, 86-97.
[http://dx.doi.org/10.1016/j.mechatronics.2018.11.008]
[48]
Liu, J.B.; Yan, B.; Zou, Q. Optimal time-distributed fast fourier transform: application to online iterative learning control-experimental high-speed nanopositioning example. Mechatronics, 2017, 41, 114-124.
[http://dx.doi.org/10.1016/j.mechatronics.2016.11.007]
[49]
Li, C.X.; Gu, G.Y.; Yang, M.J.; Zhu, L.M. High-speed tracking of a nanopositioning stage using modified repetitive control. IEEE Trans. Autom. Sci. Eng., 2017, 14, 1467-1477.
[http://dx.doi.org/10.1109/TASE.2015.2428437]
[50]
Hamed, Y.S.; Albogamy, K.M.; Sayed, M. Nonlinear vibrations control of a contact-mode AFM model via a time-delayed positive position feedback. Alexandria Eng. J., 2021, 60, 963-977.
[http://dx.doi.org/10.1016/j.aej.2020.10.024]
[51]
Soltani Bozchalooi, I.; Careaga Houck, A.; AlGhamdi, J.M.; Youcef-Toumi, K. Design and control of multi-actuated atomic force microscope for large-range and high-speed imaging. Ultramicroscopy, 2016, 160, 213-224.
[http://dx.doi.org/10.1016/j.ultramic.2015.10.016] [PMID: 26547505]
[52]
Dzedzickis, A.; Bucinskas, V.; Viržonis, D.; Sesok, N.; Ulcinas, A.; Iljin, I.; Sutinys, E.; Petkevicius, S.; Gargasas, J.; Morkvenaite-Vilkonciene, I. Modification of the AFM sensor by a precisely regulated air stream to increase imaging speed and accuracy in the contact mode. Sensors (Basel), 2018, 18(8), 2694.
[http://dx.doi.org/10.3390/s18082694] [PMID: 30115868]
[53]
Bučinskas, V.; Dzedzickis, A.; Šutinys, E.; Šešok, N.; Iljin, I. Experimental research Of improved sensor of atomic force microscope. Adv. Intellig. Syst. Comput., 2017, 543, 601-609.
[http://dx.doi.org/10.1007/978-3-319-48923-0_64]
[54]
Sadeghian, H.; Herfst, R.; Dekker, B.; Winters, J.; Bijnagte, T.; Rijnbeek, R. High-throughput atomic force microscopes operating in parallel. Rev. Sci. Instrum., 2017, 88(3), 033703.
[http://dx.doi.org/10.1063/1.4978285] [PMID: 28372370]
[55]
Xie, H.; Wen, Y.B.; Shen, X.J.; Zhang, H.; Sun, L.N. High-Speed afm imaging of nanopositioning stages using h¥ and iterative learning control. IEEE Trans. Ind. Electron., 2020, 67, 2430-2439.
[http://dx.doi.org/10.1109/TIE.2019.2902792]
[56]
Yoo, H.W.; Ito, S.; Schitter, G. High speed laser scanning microscopy by iterative learning control of a galvanometer scanner. Control Eng. Pract., 2016, 50, 12-21.
[http://dx.doi.org/10.1016/j.conengprac.2016.02.007]
[57]
Habibullah, H. A novel control approach for high-precision positioning of a piezoelectric tube scanner. IEEE Trans. Autom. Sci. Eng., 2017, 14, 325-336.
[http://dx.doi.org/10.1109/TASE.2016.2526641]
[58]
Sun, Z.Y.; Song, B.; Xi, N.; Yang, R.G.; Hao, L.N.; Yang, Y.L.; Chen, L.L. Asymmetric hysteresis modeling and compensation approach for nanomanipulation system motion control considering workingrange effect. IEEE Trans. Ind. Electron., 2017, 64, 5513-5523.
[http://dx.doi.org/10.1109/TIE.2017.2677300]
[59]
Wang, A.; Cheng, L.; Yang, C.G.; Hou, Z.G. An adaptive fuzzy predictive controller with hysteresis compensation for piezoelectric actuators. Cognit. Comput., 2020, 12, 736-747.
[http://dx.doi.org/10.1007/s12559-020-09722-8]
[60]
Zhang, W.; Li, C.; Huang, T.; He, X. Synchronization of memristor-based coupling recurrent neural networks with time-varying delays and impulses. IEEE Trans. Neural Netw. Learn. Syst., 2015, 26(12), 3308-3313.
[http://dx.doi.org/10.1109/TNNLS.2015.2435794] [PMID: 26054076]
[61]
Ruppert, M.G.; Maroufi, M.; Bazaei, A.; Moheimani, S.O.R. Kalman filter enabled high-speed control of a MEMS nanopositioner. IFAC-Papers OnLine, 2017, 50, 15554-15560.
[http://dx.doi.org/10.1016/j.ifacol.2017.08.1879]
[62]
Ruppert, M.; Karvinen, K.S.; Wiggins, S.L.; Moheimani, S.O.R. A kalman filter for amplitude estimation in high-speed dynamic mode atomic force microscopy. IEEE Trans. Contr. Syst. Technol., 2016, 24, 276-284.
[http://dx.doi.org/10.1109/TCST.2015.2435654]
[63]
Fan, B.; Yang, Q.; Jagannathan, S.; Sun, Y. Asymptotic tracking controller design for nonlinear systems with guaranteed performance. IEEE Trans. Cybern., 2018, 48(7), 2001-2011.
[http://dx.doi.org/10.1109/TCYB.2017.2726039] [PMID: 28742050]
[64]
Sun, Z.Y.; Xi, N.; Xue, Y.X.; Cheng, Y.; Chen, L.L.; Yang, R.G.; Song, B. Task space motion control for AFM-based nanorobot using optimal and ultralimit archimedean spiral local scan. IEEE Robot. Autom. Lett., 2020, 5, 282-289.
[http://dx.doi.org/10.1109/LRA.2019.2955942]
[65]
Duflot, L.A.; Reisenhofer, R.; Tamadazte, B.; Andreff, N.; Krupa, A. Wavelet and shearlet-based image representations for visual servoing. Int. J. Robot. Res., 2019, 38, 422-450.
[http://dx.doi.org/10.1177/0278364918769739]
[66]
Ourak, M.; Tamadazte, B.; Lehmann, O.; Andreff, N. Direct visual servoing using wavelet coefficients. IEEE/ASME Trans. Mechatron., 2019, 24, 1129-1140.
[http://dx.doi.org/10.1109/TMECH.2019.2898509]
[67]
Ulčinas, A.; Vaitekonis, Š. Rotational scanning atomic force microscopy. Nanotechnology, 2017, 28(10), 10LT02.
[http://dx.doi.org/10.1088/1361-6528/aa5af7] [PMID: 28106532]
[68]
Bazaei, A.; Yong, Y.K.; Moheimani, S.O.R. Combining spiral scanning and internal model control for sequential AFM imaging at video rate. Mechatron, 2017, 22, 371-380.
[http://dx.doi.org/10.1109/TMECH.2016.2574892]
[69]
Liu, Y.F.; Shan, J.J.; Meng, Y.; Zhu, D.F. Modeling and identification of asymmetric hysteresis in smart actuators: a modified MS model approach. IEEE/ASME Trans. Mechatron., 2016, 21(1), 38-43.
[http://dx.doi.org/10.1109/TMECH.2015.2500905]
[70]
Janaideh, M.A.; Rakotondrabe, M.; Aljanaideh, O. Further results on hysteresis compensation of smart micropositioning systems with the inverse Prandtl-Ishlinskii compensator. IEEE Trans. Contr. Syst. Technol., 2016, 24, 428-439.
[http://dx.doi.org/10.1109/TCST.2015.2446959]
[71]
Li, P.; Li, P.; Sui, Y. Adaptive fuzzy hysteresis internal model tracking control of piezoelectric actuators with nanoscale application. IEEE Trans. Fuzzy Syst., 2016, 24, 1246-1254.
[http://dx.doi.org/10.1109/TFUZZ.2015.2502282]
[72]
Wang, Y.; Wu, S.; Xu, L.; Zeng, Y. A new precise positioning method for piezoelectric scanner of AFM. Ultramicroscopy, 2019, 196, 67-73.
[http://dx.doi.org/10.1016/j.ultramic.2018.09.016] [PMID: 30290329]
[73]
Li, Z.; Zhang, X.Y.; Su, C.Y.; Chai, T.Y. Nonlinear control of systems preceded by Preisach hysteresis description: A prescribed adaptive control approach. IEEE Trans. Contr. Syst. Technol., 2016, 24, 451-460.
[http://dx.doi.org/10.1109/TCST.2015.2441001]
[74]
Cheng, L.; Liu, W.C.; Hou, Z.G.; Huang, T.W.; Yu, J.Z.; Tan, M. An adaptive Takagi-Sugeno fuzzy model based predictive controller for piezoelectric actuators. IEEE Trans. Ind. Electron., 2017, 64, 3048-3058.
[http://dx.doi.org/10.1109/TIE.2016.2644603]
[75]
Li, L.L.; Li, C.X.; Gu, G.Y.; Zhu, L.M. Modified repetitive control based cross-coupling compensation approach for the piezoelectric tube scanner of atomic force microscopes. IEEE/ASME Trans. Mechatron., 2019, 24, 666-676.
[http://dx.doi.org/10.1109/TMECH.2019.2893628]
[76]
Liu, Y.F.; Shan, J.J.; Meng, Y.; Zhu, D.F. Model predictive tracking control of nonholonomic mobile robots with coupled input constraints and unknown dynamics. IEEE/ASME Trans. Mechatron., 2016, 21, 38-43.
[77]
Zhang, X.Y.; Zhi, L.; Su, C.Y.; Lin, Y.; Fu, Y.L. Implementable adaptive inverse control of hysteretic systems via output feedback with application to piezoelectric positioning stages. IEEE Trans. Ind. Electron., 2016, 63, 5733-5743.
[http://dx.doi.org/10.1109/TIE.2016.2578842]
[78]
Ziegler, D.; Meyer, T.R.; Amrein, A.; Bertozzi, A.L.; Ashby, P.D. Ideal scan path for high-speed atomic force microscopy. Mechatronics, 2017, 22, 381-391.
[http://dx.doi.org/10.1109/TMECH.2016.2615327]
[79]
Yuan, S.; Wang, Z.; Liu, L.; Xi, N.; Wang, Y. Stochastic approach for feature-based tip localization and planning in nanomanipulations. IEEE Trans. Autom. Sci. Eng., 2017, 14, 1643-1654.
[http://dx.doi.org/10.1109/TASE.2017.2698003]
[80]
Chen, Y.; Liu, J.; Xie, L.; Hu, Y.; Shu, H.; Luo, L.; Zhang, L.; Gui, Z.; Coatrieux, G. Discriminative prior - prior image constrained compressed sensing reconstruction for low-dose CT imaging. Sci. Rep., 2017, 7(1), 13868.
[http://dx.doi.org/10.1038/s41598-017-13520-y] [PMID: 29066731]
[81]
Han, G.; Lin, B. Optimal sampling and reconstruction of undersampled atomic force microscope images using compressive sensing. Ultramicroscopy, 2018, 189, 85-94.
[http://dx.doi.org/10.1016/j.ultramic.2018.03.019] [PMID: 29626836]
[82]
Arildsen, T.; Oxvig, C.S.; Pedersen, P.S.; Ostergaard, J.; Larsen, T. Reconstruction algorithms in undersampled AFM imaging. Proces, 2016, 10, 31-46.
[http://dx.doi.org/10.1109/JSTSP.2015.2500363]
[83]
Luo, Y.; Andersson, S.B. Image reconstruction for sub-sampled atomic force microscopy images using deep neural networks. Micron, 2020, 130, 102814.
[http://dx.doi.org/10.1016/j.micron.2019.102814] [PMID: 31931325]
[84]
Han, G.; Lin, B.; Lin, Y. Reconstruction of atomic force microscopy image using compressed sensing. Micron, 2018, 105, 1-10.
[http://dx.doi.org/10.1016/j.micron.2017.11.003] [PMID: 29132029]
[85]
Han, G.Q.; Niu, Y.X.; Zou, Y.; Lin, B. Reconstruction of undersampled atomic force microscope images using block-based compressive sensing. Appl. Surf. Sci., 2019, 484, 797-807.
[http://dx.doi.org/10.1016/j.apsusc.2019.04.157]
[86]
Zhang, Y.X.; Li, Y.Z.; Wang, Z.Y.; Song, Z.H.; Lin, R.; Qian, J.Q.; Yao, J.E. A fast image reconstruction method based on Bayesian compressed sensing for the undersampled AFM data with noise. Meas. Sci. Technol., 2019, 30, 025402.
[http://dx.doi.org/10.1088/1361-6501/aaf4e7]
[87]
Paulo, R.; Renato, V. L1-Minimization algorithm for bayesian online compressed sensing. Entropy (Basel), 2017, 19, 667.
[http://dx.doi.org/10.3390/e19120667]
[88]
Oxvig, C.S.; Arildsen, T.; Larsen, T. Structure assisted compressed sensing reconstruction of undersampled AFM images. Ultramicroscopy, 2017, 172, 1-9.
[http://dx.doi.org/10.1016/j.ultramic.2016.09.011] [PMID: 27721127]
[89]
Liu, C.H.; Fang, Y.C.; Fan, Z.; Wang, C.; Wu, Y.N. Novel contact scanning strategy based on atomic force microscope dynamic model. Control Theory Appl., 2019, 36, 1920-1928.
[90]
Wu, Y.N.; Fang, Y.C.; Ren, X.; Lu, H. A wavelet-based AFM fast imaging method with self-tuning scanning frequency. IEEE Trans. NanoTechnol., 2017, 16, 1088-1098.
[http://dx.doi.org/10.1109/TNANO.2017.2761810]
[91]
Wu, Y.; Fang, Y.; Wang, C.; Liu, C.; Fan, Z. A high-speed atomic force microscopy with super resolution based on path planning scanning. Ultramicroscopy, 2020, 213, 112991.
[http://dx.doi.org/10.1016/j.ultramic.2020.112991] [PMID: 32334282]
[92]
Cheng, L.; Liu, W.; Yang, C.; Hou, Z.G.; Huang, T.; Tan, M. A neural-network-based controller for piezoelectric-actuated stick-slip devices. IEEE Trans. Ind. Electron., 2018, 65, 2598-2607.
[http://dx.doi.org/10.1109/TIE.2017.2740826]
[93]
He, W.; Chen, Y.; Yin, Z. Adaptive neural network control of an uncertain robot with full-state constraints. IEEE Trans. Cybern., 2016, 46(3), 620-629.
[http://dx.doi.org/10.1109/TCYB.2015.2411285] [PMID: 25850098]
[94]
Wang, J.; Cao, J.; Sherratt, R.S.; Park, J.H. An improved ant colony optimization-based approach with mobile sink for wireless sensor networks. Supercomput., 2018, 74, 6633-6645.
[http://dx.doi.org/10.1007/s11227-017-2115-6]
[95]
Luo, Y.; Andersson, S.B. A continuous sampling pattern design algorithm for atomic force microscopy images. Ultramicroscopy, 2019, 196, 167-179.
[http://dx.doi.org/10.1016/j.ultramic.2018.10.013] [PMID: 30412842]

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