Using 3D printing as a research tool for materials discovery
Tài liệu tham khảo
Zopf, 2013, Bioresorbable Airway Splint Created with a Three-Dimensional Printer, N. Engl. J. Med., 368, 2043, 10.1056/NEJMc1206319
Rael, 2018
Choong, 2020, The global rise of 3D printing during the COVID-19 pandemic, Nat. Rev. Mater., 5, 637, 10.1038/s41578-020-00234-3
Placone, 2018, Recent advances in extrusion-based 3D printing for biomedical applications, Adv. Healthc. Mater., 7, 10.1002/adhm.201701161
Duty, 2018, What makes a material printable? A viscoelastic model for extrusion-based 3D printing of polymers, J. Manuf. Process., 35, 526, 10.1016/j.jmapro.2018.08.008
Jiang, 2020, Extrusion 3D printing of polymeric materials with advanced properties, Adv. Sci., 7, 10.1002/advs.202001379
Tang, 2023, Advanced supramolecular design for direct ink writing of soft materials, Chem. Soc. Rev., 52, 1614, 10.1039/D2CS01011A
Bagheri, 2019, Photopolymerization in 3D Printing, ACS Appl. Polym. Mater., 1, 593, 10.1021/acsapm.8b00165
Corrigan, 2019, Seeing the Light: Advancing Materials Chemistry through Photopolymerization, Angew. Chem. Int. Ed., 58, 5170, 10.1002/anie.201805473
Kelly, 2019, Volumetric additive manufacturing via tomographic reconstruction, Science, 363, 1075, 10.1126/science.aau7114
Baden, 2015, Open Labware: 3-D Printing Your Own Lab Equipment, PLoS Biol., 13
Saggiomo, 2022, A 3D Printer in the Lab: Not Only a Toy, Adv. Sci., 9, 10.1002/advs.202202610
Carroll, 2017, 3D Printing of Molecular Models with Calculated Geometries and p Orbital Isosurfaces, J. Chem. Educ., 94, 886, 10.1021/acs.jchemed.6b00933
Jones, 2018, A Simplified Method for the 3D Printing of Molecular Models for Chemical Education, J. Chem. Educ., 95, 88, 10.1021/acs.jchemed.7b00533
Liu, 2011, Multifunctional Integration: From Biological to Bio-Inspired Materials, ACS Nano, 5, 6786, 10.1021/nn203250y
Davidson, 2016, Design Paradigm Utilizing Reversible Diels–Alder Reactions to Enhance the Mechanical Properties of 3D Printed Materials, ACS Appl. Mater. Interfaces, 8, 16961, 10.1021/acsami.6b05118
Lin, 2017, Ring Shuttling Controls Macroscopic Motion in a Three-Dimensional Printed Polyrotaxane Monolith, Angew. Chem. Int. Ed., 56, 4452, 10.1002/anie.201612440
Robertson, 2018, Rapid energy-efficient manufacturing of polymers and composites via frontal polymerization, Nature, 557, 223, 10.1038/s41586-018-0054-x
Hegde, 2017, 3D Printing All-Aromatic Polyimides using Mask-Projection Stereolithography: Processing the Nonprocessable, Adv. Mater., 29, 10.1002/adma.201701240
Ahn, 2020, Rapid High-Resolution Visible Light 3D Printing, ACS Cent. Sci., 6, 1555, 10.1021/acscentsci.0c00929
Tumbleston, 2015, Continuous liquid interface production of 3D objects, Science, 347, 1349, 10.1126/science.aaa2397
Robertson, 2017, Alkyl Phosphite Inhibitors for Frontal Ring-Opening Metathesis Polymerization Greatly Increase Pot Life, ACS Macro Lett., 6, 609, 10.1021/acsmacrolett.7b00270
Hou, 2021, Automatic Generation of 3D-Printed Reactionware for Chemical Synthesis Digitization using ChemSCAD, ACS Cent. Sci., 7, 212, 10.1021/acscentsci.0c01354
Ghanem, 2020, The role of polymer mechanochemistry in responsive materials and additive manufacturing, Nat. Rev. Mater., 6, 84, 10.1038/s41578-020-00249-w
Dubbin, 2016, Dual-Stage Crosslinking of a Gel-Phase Bioink Improves Cell Viability and Homogeneity for 3D Bioprinting, Adv. Healthc. Mater., 5, 2488, 10.1002/adhm.201600636
Kolesky, 2014, 3D Bioprinting of Vascularized, Heterogeneous Cell-Laden Tissue Constructs, Adv. Mater., 26, 3124, 10.1002/adma.201305506
Lee, 2019, 3D bioprinting of collagen to rebuild components of the human heart, Science, 365, 482, 10.1126/science.aav9051
Ouyang, 2016, 3D Printing of Shear-Thinning Hyaluronic Acid Hydrogels with Secondary Cross-Linking, ACS Biomater. Sci. Eng., 2, 1743, 10.1021/acsbiomaterials.6b00158
Saha, 2018, Additive Manufacturing of Catalytically Active Living Materials, ACS Appl. Mater. Interfaces, 10, 13373, 10.1021/acsami.8b02719
Hogan, 1997, Combinatorial chemistry in drug discovery, Nat. Biotechnol., 15, 328, 10.1038/nbt0497-328
Hughes, 2018, Applications of Flow Chemistry in Drug Development: Highlights of Recent Patent Literature, Org. Process Res. Dev., 22, 13, 10.1021/acs.oprd.7b00363
Meredith, 2000, Combinatorial materials science for polymer thin-film dewetting, Macromolecules, 33, 9747, 10.1021/ma001298g
Carson Meredith, 2002, Combinatorial methods for investigations in polymer materials science, MRS Bull., 27, 330, 10.1557/mrs2002.101
Fasolka, 2010, Gradient and microfluidic library approaches to polymer interfaces, 63
Berry, 2007, Versatile platform for creating gradient combinatorial libraries via modulated light exposure, Rev. Sci. Instrum., 78, 10.1063/1.2755729
Claussen, 2012, Polymer gradient materials: can nature teach us new tricks?, Macromol. Mater. Eng., 297, 938, 10.1002/mame.201200032
Atefi, 2014, High Throughput, Polymeric Aqueous Two-Phase Printing of Tumor Spheroids, Adv. Funct. Mater., 24, 6509, 10.1002/adfm.201401302
Louzao, 2018, Identification of Novel “Inks” for 3D Printing Using High-Throughput Screening: Bioresorbable Photocurable Polymers for Controlled Drug Delivery, ACS Appl. Mater. Interfaces, 10, 6841, 10.1021/acsami.7b15677
Hansen, 2013, High-Throughput Printing via Microvascular Multinozzle Arrays, Adv. Mater., 25, 96, 10.1002/adma.201203321
Manzano, 2019, High Throughput Screening of 3D Printable Resins: Adjusting the Surface and Catalytic Properties of Multifunctional Architectures, ACS Appl. Polym. Mater., 1, 2890, 10.1021/acsapm.9b00598
Stach, 2021, Autonomous experimentation systems for materials development: A community perspective, Matter, 4, 2702, 10.1016/j.matt.2021.06.036
Rooney, 2022, A self-driving laboratory designed to accelerate the discovery of adhesive materials, Digital Discovery, 1, 382, 10.1039/D2DD00029F
MacLeod, 2020, Self-driving laboratory for accelerated discovery of thin-film materials, Sci. Adv., 6, 10.1126/sciadv.aaz8867
Gongora, 2021, Using Simulation to Accelerate Autonomous Experimentation: A Case Study using Mechanics, iScience, 24, 10.1016/j.isci.2021.102262
Shields, 2021, Bayesian reaction optimization as a tool for chemical synthesis, Nature, 590, 89, 10.1038/s41586-021-03213-y
Vriza, 2023, Self-Driving Laboratory for Polymer Electronics, Chem. Mater., 35, 3046, 10.1021/acs.chemmater.2c03593
Gongora, 2020, A Bayesian experimental autonomous researcher for mechanical design, Sci. Adv., 6, eaaz1708, 10.1126/sciadv.aaz1708
Xie, 2023, A Bayesian regularization network approach to thermal distortion control in 3D printing, Comput. Mech., 72, 137, 10.1007/s00466-023-02270-6
Jin, 2019, Autonomous in-situ correction of fused deposition modeling printers using computer vision and deep learning, Manuf. Lett., 22, 11, 10.1016/j.mfglet.2019.09.005
Erps, 2021, Accelerated discovery of 3D printing materials using data-driven multiobjective optimization, Sci. Adv., 7, eabf7435, 10.1126/sciadv.abf7435
Haque, 2022, Defining the Macromolecules of Tomorrow through Synergistic Sustainable Polymer Research, Chem. Rev., 122, 6322, 10.1021/acs.chemrev.1c00173
Sanchez-Rexach, 2020, Sustainable Materials and Chemical Processes for Additive Manufacturing, Chem. Mater., 32, 7105, 10.1021/acs.chemmater.0c02008
Chyr, 2023, Review of high-performance sustainable polymers in additive manufacturing, Green Chem., 25, 453, 10.1039/D2GC03474C
Faludi, 2019, Novel materials can radically improve whole-system environmental impacts of additive manufacturing, J. Clean. Prod., 212, 1580, 10.1016/j.jclepro.2018.12.017
Butler, 2018, Machine learning for molecular and materials science, Nature, 559, 547, 10.1038/s41586-018-0337-2
Ni, 2023, Generative design of de novo proteins based on secondary-structure constraints using an attention-based diffusion model, Chem, 10.1016/j.chempr.2023.03.020
Hart, 2021, Machine learning for alloys, Nat. Rev. Mater., 6, 730, 10.1038/s41578-021-00340-w
Zhang, 2022, Efficient pneumatic actuation modeling using hybrid physics-based and data-driven framework, Cell Rep. Phys. Sci., 3
Zheng, 2020, Machine learning-based detection of graphene defects with atomic precision, Microbios Lett., 12, 181
Creswell, 2018, Generative adversarial networks: An overview, IEEE Signal Process. Mag., 35, 53, 10.1109/MSP.2017.2765202
Zheng, 2022, Designing mechanically tough graphene oxide materials using deep reinforcement learning, npj Comput. Mater., 8, 225, 10.1038/s41524-022-00919-z
Theodoridis, 2015
Gao, 2015, The status, challenges, and future of additive manufacturing in engineering, Comput. Aided Des., 69, 65, 10.1016/j.cad.2015.04.001
Jin, 2021, Precise localization and semantic segmentation detection of printing conditions in fused filament fabrication technologies using machine learning, Addit. Manuf., 37
Li, 2019, Bio-inspired Design and Additive Manufacturing of Soft Materials, Machines, Robots, and Haptic Interfaces, Angew. Chem. Int. Ed., 58, 11182, 10.1002/anie.201813402
Gu, 2018, Bioinspired hierarchical composite design using machine learning: simulation, additive manufacturing, and experiment, Mater. Horiz., 5, 939, 10.1039/C8MH00653A
Lee, 2022, Generative machine learning algorithm for lattice structures with superior mechanical properties, Mater. Horiz., 9, 952, 10.1039/D1MH01792F
Gongora, 2022, Designing lattices for impact protection using transfer learning, Matter, 5, 2829, 10.1016/j.matt.2022.06.051
Lee, 2023, Deep Learning Accelerated Design of Mechanically Efficient Architected Materials, ACS Appl. Mater. Interfaces, 15, 22543, 10.1021/acsami.3c02746
Garland, 2021, Pragmatic generative optimization of novel structural lattice metamaterials with machine learning, Mater. Des., 203, 10.1016/j.matdes.2021.109632
Yang, 2022, Deep learning-based X-ray computed tomography image reconstruction and prediction of compression behavior of 3D printed lattice structures, Addit. Manuf., 54
Shen, 2022, Nature-inspired architected materials using unsupervised deep learning, Commun. Eng., 1, 37, 10.1038/s44172-022-00037-0
Gerdes, 2023, Monitoring and control of biological additive manufacturing using machine learning, J. Intell. Manuf., 1-23
Kim, 2021, Additive manufacturing of functional microarchitected reactors for energy, environmental, and biological applications, Clin. Nutr. Res., 10, 303, 10.7762/cnr.2021.10.4.303
Kuschmitz, 2021, Design and additive manufacturing of porous sound absorbers—A machine-learning approach, Materials, 14, 1747, 10.3390/ma14071747
Stoyanov, 2017, 1
Lee, 2020, Automated detection of part quality during two-photon lithography via deep learning, Addit. Manuf., 36
Jin, 2020, Machine learning for advanced additive manufacturing, Matter, 3, 1541, 10.1016/j.matt.2020.08.023
Zhang, 2019, In-Process monitoring of porosity during laser additive manufacturing process, Addit. Manuf., 28, 497
Jin, 2021, Monitoring anomalies in 3D bioprinting with deep neural networks, ACS Biomater. Sci. Eng.
Xiong, 2022, Intelligent additive manufacturing and design state of the art and future perspectives, Addit. Manuf., 59
