Citation: LIANG Heng, LUO Yuanyuan. Common mathematical-physical essence involved from chromatographic separation to intelligent medicine: irreversibility[J]. Chinese Journal of Chromatography, ;2019, 37(4): 367-375. doi: 10.3724/SP.J.1123.2018.12008 shu

Common mathematical-physical essence involved from chromatographic separation to intelligent medicine: irreversibility

LIANG Heng ,
LUO Yuanyuan

The Key Laboratory of Biomedical Information Engineering of Education Ministry, Separation Science Institute, Xi'an Jiaotong University, Xi'an 710049, China

Received Date: 5 December 2018

Fund Project: National Natural Science Foundation of China (No. 21377102)

The chromatographic separation processes for many molecules in solute bands by stationary phase-mobile phase separation can be compared to ordering disease severity. The common features of chromatographic processes and machine-disease diagnosis-doctor's advice is the separation (classification) of components (individual disease states), both of which show irreversibility of time evolution; however, the former is caused by linear non-equilibrium thermodynamics and latter by nonlinear non-equilibrium thermodynamics (dissipative structure). When the scientific view is extended from drug detection and preparation to evidence-based medicine (EBM), discrete mathematics (axiomatic set theory and probability measure), and artificial intelligence (AI) cloud computing, the convection-diffusion equation and irreversibility in non-equilibrium thermodynamics form a common and core essence of mathematical physics that crosses fields of chromatographic separation and intelligent medicine. It is of profound scientific and practical significance to construct and develop a unified and all-encompassing mathematical framework by incorporating the generality and characteristics of the two subjects.
- nonequilibrium thermodynamic separation theory (NTST),
- nonequilibrium thermodynamics,
- irreversibility,
- evidence-based medicine (EBM),
- artificial intelligence (AI),
- disease states,
- mathematical framework,
- review
1. [1]
2. [2]
3. [3]
4. [4]
5. [5]
6. [6]
7. [7]
8. [8]
9. [9]
10. [10]
11. [11]
12. [12]
13. [13]
14. [14]
15. [15]
16. [16]
17. [17]
18. [18]
19. [19]
20. [20]
21. [21]
22. [22]
23. [23]
24. [24]
25. [25]
26. [26]
27. [27]
28. [28]
29. [29]
30. [30]
31. [31]
32. [32]
33. [33]
34. [34]
35. [35]
36. [36]
37. [37]
38. [38]
39. [39]
40. [40]
41. [41]
42. [42]
43. [43]
44. [44]
45. [45]
46. [46]
47. [47]
48. [48]
49. [49]
50. [50]
51. [51]
52. [52]
53. [53]
54. [54]
55. [55]
56. [56]
57. [57]
58. [58]
59. [59]
60. [60]
61. [61]
62. [62]
63. [63]
1. [1]
  Yifan Liu , Haonan Peng . AI-Assisted New Era in Chemistry: A Review of the Application and Development of Artificial Intelligence in Chemistry. University Chemistry, 2025, 40(7): 189-199. doi: 10.12461/PKU.DXHX202405182
2. [2]
  Weigang Zhu , Jianfeng Wang , Qiang Qi , Jing Li , Zhicheng Zhang , Xi Yu . Curriculum Development for Cheminformatics and AI-Driven Chemistry Theory toward an Intelligent Era. University Chemistry, 2025, 40(9): 34-42. doi: 10.12461/PKU.DXHX202412002
3. [3]
  Meirong Cui , Mo Xie , Jie Chao . Design and Reflections on the Integration of Artificial Intelligence in Physical Chemistry Laboratory Courses. University Chemistry, 2025, 40(5): 291-300. doi: 10.12461/PKU.DXHX202412015
4. [4]
  Cheng-an Tao , Jian Huang , Yujiao Li . Exploring the Application of Artificial Intelligence in University Chemistry Laboratory Instruction. University Chemistry, 2025, 40(9): 5-10. doi: 10.12461/PKU.DXHX202408132
5. [5]
  Lei Qin , Kai Guo . Application of Generative Artificial Intelligence in the Simulation of Acid-Base Titration Images. University Chemistry, 2025, 40(9): 11-18. doi: 10.12461/PKU.DXHX202408123
6. [6]
  Xiao Ma , Junjie Wang , Xin Chen , Jingcheng Li , Lihong Zhao , Xueping Sun , Shaojuan Cheng , Fang Wang . Exploring Innovative Approaches to Chemistry Instructional Organization Driven by Artificial Intelligence. University Chemistry, 2025, 40(9): 99-106. doi: 10.12461/PKU.DXHX202410085
7. [7]
  Run Yang , Huajie Pang , Huiping Zang , Ruizhong Zhang , Zhicheng Zhang , Xiyan Li , Libing Zhang . Artificial Intelligence-Enabled DNA Computing: Exploring New Frontiers in Bioinformatics. University Chemistry, 2025, 40(9): 107-117. doi: 10.12461/PKU.DXHX202412135
8. [8]
  Yan Zhang , Limin Zhou , Xiaoyan Cao , Mutai Bao . Exploring the Application of Artificial Intelligence in Marine-Themed Integrated Physical Chemistry Experiments. University Chemistry, 2025, 40(9): 118-125. doi: 10.12461/PKU.DXHX202503062
9. [9]
  Wuyi Feng , Di Zhao . Significance and Measures of Integrating Artificial Intelligence Technology into College Chemistry Teaching. University Chemistry, 2025, 40(9): 156-163. doi: 10.12461/PKU.DXHX202502107
10. [10]
  Lingli Wu , Shengbin Lei . Generative AI-Driven Innovative Chemistry Teaching: Current Status and Future Prospects. University Chemistry, 2025, 40(9): 206-219. doi: 10.12461/PKU.DXHX202503069
11. [11]
  Yuejiao Wang , Quanxing Mao , Junshuo Cui , Xiaogeng Feng , Xiaohong Chang , Zhenning Lou , Ying Xiong . 颜色识别式人工智能融入混合碱滴定实验的应用. University Chemistry, 2026, 41(1): 107-113. doi: 10.12461/PKU.DXHX202506020
12. [12]
  Huixin Dong , Zhenlei Zhou , Wenxin Zou , Juan Jin , Xiguang Liu , Yuzhong Niu , Lili Zhu , Hua Jiang . Exploration and Practice of Ideological and Political Education in Inorganic Chemistry Courses with the Assistance of Artificial Intelligence. University Chemistry, 2026, 41(3): 254-261. doi: 10.12461/PKU.DXHX202505003
13. [13]
  Haoran Zhang , Yaxin Jin , Peng Kang , Sheng Zhang . The Convergence and Innovative Application of Artificial Intelligence in Scientific Research: A Case Study of Electrocatalytic Carbon Dioxide Reduction in the Context of the Dual-Carbon Strategy. University Chemistry, 2025, 40(9): 148-155. doi: 10.12461/PKU.DXHX202412099
14. [14]
  Ping Li , Chao Yin . Teaching Exploration and Practical Innovation of General Education Courses in the Context of Artificial Intelligence. University Chemistry, 2024, 39(10): 402-407. doi: 10.12461/PKU.DXHX202403075
15. [15]
  Yu Fang . AI-Empowered Education: A Case Study of Self-Directed Learning with ChatGPT-4. University Chemistry, 2025, 40(9): 1-4. doi: 10.12461/PKU.DXHX202502013
16. [16]
  Jizhu Zhou , Xiao Wei , Kaixue Wang . Deep Integration of Artificial Intelligence and Chemical Research: Applying the Fourth Paradigm and Evolving the Role of Chemists. University Chemistry, 2025, 40(12): 119-125. doi: 10.12461/PKU.DXHX202509003
17. [17]
  Qifeng Zheng , Aimei Gao , Ruirui Zhao . Exploring a New Undergraduate Training Model for Chemistry Majors through “Science-Education-Innovation Integration” in the AI Era. University Chemistry, 2026, 41(2): 161-167. doi: 10.12461/PKU.DXHX202502056
18. [18]
  Tianlong Zhang , Rongling Zhang , Hongsheng Tang , Yan Li , Hua Li . Exploration on the Integration Mode of Instrumental Analysis with Science and Education under the Background of Artificial Intelligence Era. University Chemistry, 2024, 39(8): 365-374. doi: 10.12461/PKU.DXHX202403014
19. [19]
  Liangjun Chen , Yu Zhang , Zhicheng Zhang , Yongwu Peng . AI-Empowering Reform in University Chemistry Education: Practical Exploration of Cultivating Informationization and Intelligent Literacy. University Chemistry, 2025, 40(9): 220-227. doi: 10.12461/PKU.DXHX202503124
20. [20]
  Ying Chen , Ronghua Yan , Weiyan Yin . Research Progress on the Synthesis of Metal Single-Atom Catalysts and Their Applications in Electrocatalytic Hydrogen Evolution Reactions. University Chemistry, 2025, 40(9): 344-353. doi: 10.12461/PKU.DXHX202503066

Get Citation

PDF

XML

Metrics

PDF Downloads(5)
Abstract views(1078)
HTML views(124)

通讯作者: 陈斌, bchen63@163.com

1.
沈阳化工大学材料科学与工程学院沈阳 110142