Solution-based Chemical Strategies to Purposely Control the Microstructure of Functional Materials
Bibliographic record
Abstract
Dr. Dongfeng Xue was born in Henan, China, in 1968. He received his B.Eng. from Henan University in 1993, then worked as a five-year graduate student at Changchun Institute of Applied Chemistry, Chinese Academy of Science. After receiving his Ph.D. degree, in Inorganic Chemistry in 1998, he spent one year as an Alexander von Humboldt research fellow at Fachbereich Physik, Universitaet Osnabruck, Germany. He was a visiting researcher in University of Ottawa (2000–2001), and the JSPS (Japan Society for the Promotion of Science) fellow at National Institute for Materials Science (2001–2003). Since August 2003, he has been a full professor at Department of Materials Science and Chemical Engineering, Dalian University of Technology, China. His research interests include crystallography, crystal growth, structure-property relationship of crystal materials, calculation and simulation of functional materials, and chemical synthesis of condensed matter. He has already published more than 200 scientific papers and more than 100 scientific reports in various national and international journals and conferences. He is a member of the editorial board of Nanoscience and Nanotechnology Letters (American Scientific Publishers).
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".