Rapid screening of the stability of polyacrylamide-based hydrogel coatings via droplet microarray analysis and interpretable machine learning
Source abstract
Abstract
The unsatisfactory stability of hydrogel coatings hinders their functional and service performance. Until now, the development of high-performance hydrogel coatings largely relies on the intuition and prior experience of researchers. Machine learning, as a powerful engine for material design, was demonstrated to accelerate the development of hydrogels with desired properties. However, the scarcity of labeled data of the target property is a fundamental challenge. Herein, we develop a miniaturized high-throughput evaluation method of hydrogel coatings. This method achieved a rapid and parallel investigation of the stability of a large number of unique acrylamide-based hydrogel coatings. Moreover, a list of main feature descriptors was screened and their quantitative contributions to coating stability were analyzed via interpretable machine learning technology. A new ternary hydrogel coating was prepared to validate the accuracy of the machine learning strategy. This advanced methodology facilitated the rational design of high-performance hydrogel coatings.
Source: openalex_inverted_index · Quality: baseline
Research context
OpenAlex topics: Hydrogels: synthesis, properties, applications · Polymer Surface Interaction Studies · 3D Printing in Biomedical Research
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (49)
Show source-provided references
1. Taylor, D. & Panhuis, M. Self-healing hydrogels. Adv. Mater. 28, 9060–9093 (2016).10.1002/adma.201601613 ↗
2. Gao, L. et al. Multi-responsive, bidirectional large deformation Bending actuators based borax cross-linked polyvinyl alcohol derivative hydrogel. RSC Adv. 162, 40005 (2017).10.1039/C7RA06617A ↗
3. Xie, W. et al. Injectable and self-healing thermosensitive magnetic hydrogel for asynchronous control release of doxorubicin and docetaxel to treat triple-negative breast cancer. ACS Appl. Mater. Interfaces 9, 33660–33673 (2017).10.1021/acsami.7b10699 ↗
4. Xu, R. et al. A universal strategy for growing a tenacious hydrogel coating from asticky initiation layer. Adv. Mater. 34, 2108889 (2012).10.1002/adma.202108889 ↗
5. Magin, C., Cooper, S. & Brennan, A. Non-toxic antifouling strategies. Mater. Today 13, 36–44 (2010).10.1016/S1369-7021(10)70058-4 ↗
6. Hua, Y. et al. Ultrafast, tough, and adhesive hydrogel based on hybrid photocrosslinking for articular cartilage repair in water-filled arthroscopy. Sci. Adv. 7, eabg0628 (2021).10.1126/sciadv.abg0628 ↗
7. Liu, J., Qu, S., Suo, Z. & Yang, W. Functional hydrogel coatings. Natl. Sci. Rev. 8, nwaa254 (2021).10.1093/nsr/nwaa254 ↗
8. Li, S. et al. Anti-swelling, and antibacterial hydrogels for tooth-extraction wound healing. Adv. Healthc. Mater. 13, 2400089 (2024).10.1002/adhm.202400089 ↗
9. Ren, Z. et al. Strong and anti-swelling nanofibrous hydrogel composites inspired by biological tissue for amphibious motion sensors. Mater. Horiz. 11, 5600–5613 (2024).10.1039/D4MH01025F ↗
10. Mredha, M. et al. Hydrogels with superior mechanical properties from the synergistic effect in hydrophobic–hydrophilic copolymers. Chem. Eng. J. 362, 325–338 (2019).10.1016/j.cej.2018.12.023 ↗
11. Guan, X. et al. Preparation of environmental resistance and anti-swelling hydrogel through solvent displacement for monitoring human health and movement in amphibious environment. Chem. Eng. J. 505, 159838 (2025).10.1016/j.cej.2025.159838 ↗
12. Haraguchi, K., Xu, Y. & Li, G. Molecular characteristics of poly(N-isopropylacrylamide) separated from nanocomposite gels by removal of clay from the polymer/clay network. Macromol. Rapid Commun. 31, 718–723 (2010).10.1002/marc.200900819 ↗
13. Dai, S. et al. Tough hydrogel–elastomer hybrids hydrophobically regulated by an MXene for motion monitoring in harsh environments. J. Mater. Chem. C. 11, 2688–2694 (2023).10.1039/D2TC04946E ↗
14. Li, M. et al. Recent advances in hydrogel-based flexible strain sensors for harsh environment applications. Chem. Sci. 15, 17799 (2024).10.1039/D4SC05295A ↗
15. Butler, K. et al. Machine learning for molecular and materials science. Nature 559, 547–555 (2018).10.1038/s41586-018-0337-2 ↗
16. Xu, T. Accelerating the prediction and discovery of peptide hydrogels with human-in-the-loop. Nat. Commun. 14, 3880 (2023).10.1038/s41467-023-39648-2 ↗
17. Xu, S. et al. Integrating machine learning for the optimization of polyacrylamide/alginate hydrogel. Regen. Biomater. 11, rbae109 (2024).10.1093/rb/rbae109 ↗
18. Yang, J. et al. High-throughput screening of zwitterion-based coatings towards improved mechanical stability and drug-loading capacity. npj Mater. Degrad. 7, 51 (2023).10.1038/s41529-023-00362-5 ↗
19. Sánchez-Morán, H., Kaar, J. & Schwartz, D. Combinatorial high-throughput screening of complex polymeric enzyme immobilization supports. J. Am. Chem. Soc. 146, 9112–9123 (2024).10.1021/jacs.3c14273 ↗
20. Yang, J. et al. Synergistic D-amino acids based antimicrobial cocktails formulated via high-throughput screening and machine learning. Adv. Sci. 11, 2307173 (2024).10.1002/advs.202307173 ↗
21. Zhong, M. et al. Accelerated discovery of CO2 electrocatalysts using active machine learning. Nature 581, 178–183 (2020).10.1038/s41586-020-2242-8 ↗
22. Sun, S. et al. Accelerated development of perovskite-inspired materials via high-throughput synthesis and machine-learning diagnosis. Joule 3, 1437–1451 (2019).10.1016/j.joule.2019.05.014 ↗
23. Dave, A. et al. Autonomous optimization of non-aqueous Li-ion battery electrolytes via robotic experimentation and machine learnin coupling. Nat. Commun. 13, 5454 (2022).10.1038/s41467-022-32938-1 ↗
24. Seifermann, M. et al. High-throughput synthesis and machine learning assisted design of photodegradable hydrogels. Small Methods 7, 2300553 (2023).10.1002/smtd.202300553 ↗
25. Rosenfeld, A. et al. Miniaturized high-throughput synthesis and screening of responsive hydrogels using nanoliter compartments. Mater. Today Bio 6, 100053 (2020).10.1016/j.mtbio.2020.100053 ↗
26. Yan, P. et al. Water dynamics in polyacrylamide hydrogels. J. Am. Chem. Soc. 140, 9466–9477 (2018).10.1021/jacs.8b03547 ↗
27. Jiao, Z. et al. Drug-free contact lens based on quaternized chitosan and tannic acid for bacterial keratitis therapy and corneal repair. Carbohydr. Polym. 286, 119314 (2022).10.1016/j.carbpol.2022.119314 ↗
28. Sun, J. et al. Highly stretchable and tough hydrogels. Nature 489, 133–136 (2021).10.1038/nature11409 ↗
29. Chan, D. et al. Combinatorial polyacrylamide hydrogels for preventing biofouling on implantable biosensors. Adv. Mater. 34, 2109764 (2022).10.1002/adma.202109764 ↗
30. Chou, Y. et al. Ultra-low fouling and high antibody loading zwitterionic hydrogel coatings for sensing and detection in complex media. Acta Biomater. 40, 31–37 (2016).10.1016/j.actbio.2016.04.023 ↗
31. Yu, Y. et al. Slippery, and anticoagulant double-network hydrogel coating. ACS Appl. Polym. Mater. 4, 5941–5951 (2022).10.1021/acsapm.2c00800 ↗
32. Li, M. et al. Hydrogel coatings of implants for pathological bone repair. Adv. Healthc. Mater. 13, 2401296 (2024).10.1002/adhm.202401296 ↗
33. Yao, M. et al. Microgel reinforced zwitterionic hydrogel coating for blood-contacting biomedical devices. Nat. Commun. 13, 5339 (2022).10.1038/s41467-022-33081-7 ↗
34. Bentéjac, C., Csörgo, A. & Martínez-Muñoz, G. A comparative analysis of gradient boosting algorithms. Artif. Intell. Rev. 54, 1937–1967 (2021).10.1007/s10462-020-09896-5 ↗
35. Danielsen, S. et al. Molecular characterization of polymer networks. Chem. Rev. 121, 5042–5092 (2021).10.1021/acs.chemrev.0c01304 ↗
36. Xiao, Z. et al. Adhesion mechanism and application progress of hydrogels. Eur. Polym. J. 173, 111277 (2022).10.1016/j.eurpolymj.2022.111277 ↗
37. Yang, J. et al. Hydrogel adhesion: A supramolecular synergy of chemistry. Adv. Funct. Mater. 30, 1901693 (2020).10.1002/adfm.201901693 ↗
38. Yang, J. et al. Combinatorial discovery and investigation of the synergism of green amino acid corrosion inhibitors: integrating high-throughput experiments and interpretable machine learning approach. Corros. Sci. 245, 112675 (2025).10.1016/j.corsci.2025.112675 ↗
39. Liu, S. et al. Towards understanding and prediction of corrosion degradation of organic coatings under tropical marine atmospheric environment via a data-driven approach. Int. J. Miner. Metall. Mater. 32, 1151–1161 (2025).10.1007/s12613-024-3045-y ↗
40. Zhang, H. et al. Empowering the Sustainable Development of High-End Alloys via Interpretive Machine Learning. Adv. Mater. 36, 2404478 (2024).10.1002/adma.202404478 ↗
41. Dong, L. et al. Hydrogel antifouling coating with highly adhesive ability via lipophilic monomer. Macromol. Mater. Eng. 307, 2100812 (2022).10.1002/mame.202100812 ↗
42. Jiang, L. et al. Real-time monitoring of hydrophobic aggregation reveals a critical role of cooperativity in hydrophobic effect. Nat. Commun. 8, 15639 (2017).10.1038/ncomms15639 ↗
43. Todeschini, R. & Consonni, V. Handbook of chemoinformatics: from data to knowledge (ed. Gasteiger, J.) 1004–1033 (Wiley-VCH, 2003).10.1002/9783527618279.ch37 ↗
44. Kim, S., Schroeder, M. & Jackson, N. Functional monomer design for synthetically accessible polymers. Chem. Sci. 16, 4755–4767 (2025).10.1039/D4SC08617A ↗
45. Cui, H. et al. Hydrophobic hydrogels as internal curing agent for concrete: the double benefit of super high water content and excellent anti-ion permeability. Compos. Commun. 33, 101236 (2022).10.1016/j.coco.2022.101236 ↗
46. You, H. et al. A model with contact maps at both polymer chain and network scales for tough hydrogels with chain entanglement, hidden length and unconventional network topology. Int. J. Mech. Sci. 262, 108713 (2024).10.1016/j.ijmecsci.2023.108713 ↗
47. Han, Z. et al. A versatile hydrogel network-repairing strategy achieved by the covalent-like hydrogen bond interaction. Sci. Adv. 8, eabl5066 (2022).10.1126/sciadv.abl5066 ↗
48. Feng, R. et al. Mechanisms of efficient polyacrylamide degradation: from multi-omics analysis to structural characterization of two amidohydrolases. Int. J. Biol. Macromol. 281, 136329 (2024).10.1016/j.ijbiomac.2024.136329 ↗
49. Wu, Z. et al. Leveraging language model for advanced multiproperty molecular optimization via prompt engineering. Nat. Mach. Intell. 6, 1359–1369 (2024).10.1038/s42256-024-00916-5 ↗