Machine learning-optimized pre-carbonization process for sugar-based carbon fibers
Bibliographic record
Abstract
No abstract is available for this record. The assistant can identify its topic from the title, but cannot infer the study’s results.
Source: Not provided · Quality: missing
Research context
OpenAlex topics: Fiber-reinforced polymer composites · Supercapacitor Materials and Fabrication · Thermochemical Biomass Conversion Processes
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (35)
Show source-provided references
1. Cao LY, Liu YS, Zhang YH, Cao YJ, Li JX, Cheng J, Zhang L, Qi Z (2022) Thermal conductivity and bending strength of SiC composites reinforced by pitch-based carbon fibers. J Adv Ceram 11(2):247–26210.1007/s40145-021-0527-5 ↗
2. Khayyam H, Jazar RN, Nunna S, Golkarnarenji G, Badii K, Fakhrhoseini SM, Kumar S, Naebe M (2020) PAN precursor fabrication, applications and thermal stabilization process in carbon fiber production: experimental and mathematical modelling. Prog Mater Sci 107:10057510.1016/j.pmatsci.2019.100575 ↗
3. Bai YX, Zhang RF, Ye X, Zhu ZX, Xie HH, Shen BY, Cai D, Liu BF, Zhang CX, Jia Z, Zhang SL, Li XD, Wei F (2018) Carbon nanotube bundles with tensile strength over 80 GPa. Nat Nanotech 13:589–59510.1038/s41565-018-0141-z ↗
4. Choi D, Kil H-S, Lee S (2019) Fabrication of low-cost carbon fibers using economical precursors and advanced processing technologies. Carbon 142:610–64910.1016/j.carbon.2018.10.028 ↗
5. Li KM, Ni XP, Wu QQ, Yuan CS, Li CL, Li D, Chen HF, Lv YG, Ju AQ (2022) Carbon-Based fibers: Fabrication, characterization and application. Adv Fiber Mater 4:631–68210.1007/s42765-022-00134-x ↗
6. Chen S, Qiu L, Cheng H-M (2020) Carbon-based fibers for advanced electrochemical energy storage devices. Chem Rev 120(5):2811–287810.1021/acs.chemrev.9b00466 ↗
7. Pacala S, Socolow R (2004) Stabilization wedges: solving the climate problem for the next 50 years with current technologies. Science 305:968–97210.1126/science.1100103 ↗
8. Souto F, Calado V, Pereira N (2018) Lignin-based carbon fiber: a current overview. Mater Res Express 5(7):07200110.1088/2053-1591/aaba00 ↗
9. Sun BQ, Chen DM, Cheng Y, Fei WD, Jiang DY, Tang SF, Zhao GD, Song JT, Hou CL, Zhang WZ, Wu SQ, Yang Y, Tan MY, Zhang J, Wei DQ, Guo CW, Zhang W, Dong S, Du SY, Han JC, Luo J, Zhang XH (2022) Sugar-Derived isotropic nanoscale polycrystalline graphite capable of considerable plastic deformation. Adv Mater 34(31):220036310.1002/adma.202200363 ↗
10. Zhang WZ, Tan MY, Chen DM, Sun BQ, Cheng Y, Xun LC, Hu JS, Hou CL, Liu YX, Fang YH, Hu P, Han WB, Dong S, Du SY, Han JC, Miao SX, Yang QD, Zhou YC, Zhang XH (2024) Sugar-derived nanocrystalline graphite matrix C/C composites with excellent ablative resistance at 3000 ˚C. Adv Mater 36(7):230989910.1002/adma.202309899 ↗
11. Tan MY, Chen DM, Cheng Y, Sun HQ, Chen GQ, Dong S, Zhao GD, Sun BQ, Wu SQ, Zhang WZ, Han JC, Han WB, Zhang XH (2022) Anisotropically oriented carbon films with Dual-Function of efficient heat dissipation and excellent electromagnetic interference shielding performances. Adv Funct Mater 32(31):220205710.1002/adfm.202202057 ↗
12. Wu SQ, Chen DM, Zhao GD, Cheng Y, Sun BQ, Yan XJ, Han WB, Chen GQ, Zhang XH (2022) Controllable synthesis of a robust sucrose-derived bio-carbon foam with 3D hierarchical porous structure for thermal insulation, flame retardancy and oil absorption. Chem Eng J 434:13451410.1016/j.cej.2022.134514 ↗
13. Yang Y, Chen DM, Chen Y, Sun BQ, Zhao GD, Fei WD, Han WB, Han JC, Zhang XH (2022) Eco-friendly and sustainable approach of assembling sugars into biobased carbon fibers. Green Chem 24(13):5097–510610.1039/D2GC01075E ↗
14. Wang YF, Goh B, Nelaturu P, Duong T, Hassan N, David R, Moorehead M, Chaudhuri S, Creuziger A, Hattrick-Simpers J, Thoma DJ, Sridharan K, Couer A (2022) Integrated High-Throughput and machine learning methods to accelerate discovery of molten salt Corrosion-Resistant alloys. Adv Sci 9(20):220037010.1002/advs.202200370 ↗
15. Che RC, Wu ZC, Quan B, Zhang RX, Zhang HR, Zhang JC, Liu WC (2023) Machine Learning-Directed fast and High-Throughput acquisition of High-Efficiency microwave absorbents from infinite design space. Adv Funct Mater 33(50):230310810.1002/adfm.202303108 ↗
16. Rickman JM, Chan HM, Harmer MP, Smeltzer JA, Marvel CJ, Roy J, Balasubramanian G (2019) Materials informatics for the screening of multi-principal elements and high-entropy alloys. Nat Commun 10:261810.1038/s41467-019-10533-1 ↗
17. He JY, Li ZZ, Zhao PL, Zhang HM, Zhang F, Wang L, Cheng XW (2024) Machine learning-assisted design of high-entropy alloys with superior mechanical properties. J Mater Res Technol 33:260–28610.1016/j.jmrt.2024.09.014 ↗
18. Wen C, Zhang Y, Wang CX, Huang HY, Wu Y, Lookman T, Su YJ (2025) Machine-Learning-Assisted compositional design of refractory High-Entropy alloys with optimal strength and ductility. Engineering 46:214–22310.1016/j.eng.2023.11.026 ↗
19. Wen C, Zhang Y, Wang CX, Xue DZ, Bai Y, Antonov S, Dai LH, Lookman T, Su YJ (2019) Machine learning assisted design of high entropy alloys with desired property. Acta Mater 170:109–11710.1016/j.actamat.2019.03.010 ↗
20. Wu QF, Wang ZJ, Hu XB, Tao Z, Yang ZS, He F, Li JJ, Wang JC (2020) Uncovering the eutectics design by machine learning in the Al-Co-Cr-Fe-Ni high entropy system. Acta Mater 182:278–28610.1016/j.actamat.2019.10.043 ↗
21. Jang J, Jo J, Kim J, Lee S, Lee T (2025) State of health Estimation of lithium-ion battery cell based on optical thermometry with physics-informed machine learning. Eng Appl Artif Intel 140:10970410.1016/j.engappai.2024.109704 ↗
22. Li JR, Huang XF, Tang XP, Guo JH, Shen QY, Chai Y, Lu W, Wang T, Liu YS (2023) The state-of-charge predication of lithium-ion battery energy storage system using data-driven machine learning. Sustain Energy Grids 34:101020
23. Liow CH, Kang H, Kim S, Na M, Lee Y, Baucour A, Bang K, Shim Y, Choe J, Hwang G, Cho S, Park G, Yeom J, Agar JC, Yuk JM, Shin J, Lee HM, Byon HR, Cho EA, Hong S (2022) Machine learning assisted synthesis of lithium-ion batteries cathode materials. Nano Energy 98:10721410.1016/j.nanoen.2022.107214 ↗
24. Li RS, Zhao WY, Li RM, Gan CL, Chen L, Wang ZT, Yang XW (2025) Leveraging machine learning for accelerated materials innovation in lithium-ion battery: a review. J Energy Chem 106:44–6210.1016/j.jechem.2025.02.038 ↗
25. Golkarnarenji G, Naebe M, Badii K, Milani AS, Jazar RN, Khayyam H (2018) Support vector regression modelling and optimization of energy consumption in carbon fiber production line. Comput Chem Eng 109:276–28810.1016/j.compchemeng.2017.11.020 ↗
26. Shirolkar N, Patwardhan P, Rahman A, Spear A, Kumar S (2021) Investigating the efficacy of machine learning tools in modeling the continuous stabilization and carbonization process and predicting carbon fiber properties. Carbon 174:605–61610.1016/j.carbon.2020.12.044 ↗
27. Guo Y, Jiang S, Fu J, Yang Y, Bao Y, Jin X (2023) Stabilization temperature prediction in carbon fiber production using empirical mode decomposition and long short-term memory network. J Clean Prod 429:13934510.1016/j.jclepro.2023.139345 ↗
28. Xiao C, Hao K, Ding Y (2014) The Bi-Directional prediction of carbon fiber production using a combination of improved particle swarm optimization and support vector machine. Materials 8:117–13610.3390/ma8010117 ↗
29. Wang J, Li L, Niu D, Tan Z (2012) An annual load forecasting model based on support vector regression with differential evolution algorithm. Appl Energ 94:65–7010.1016/j.apenergy.2012.01.010 ↗
30. Chen KY, Wang CH (2007) Support vector regression with genetic algorithms in forecasting tourism demand. Tourism Manage 28:215–22610.1016/j.tourman.2005.12.018 ↗
31. Smola AJ, Scholkopf B (2004) A tutorial on support vector regression. Stat Comput 14(3):199–22210.1023/B:STCO.0000035301.49549.88 ↗
32. Liao Z, Huang Y, Yue X, Lu H, Xuan P, Ju Y (2016) In silico prediction of gamma-aminobutyric acid type-a receptors using novel machine-learning-based SVM and GBDT approaches. Biomed Res Int 2016:237526810.1155/2016/2375268 ↗
33. Sigrist F (2021) Gradient and Newton boosting for classification and regression. Expert Syst Appl 167:11408010.1016/j.eswa.2020.114080 ↗
34. Zhou W, Zhang H, Yu Y, Zou X, Shi J, Zhao Y, Ye Y (2020) Dissolution mechanism of sodium alginate and properties of its regenerated fiber under low temperature. Int J Biol Macromol 16:2810–2819
35. An F, Zhou PC, Lu CX, Liu YD (2019) Tuning the surface grooves of carbon fibers by dry-jet gel-spinning. Carbon 143:200–20310.1016/j.carbon.2018.11.009 ↗