A Two‐Decade Journey of Continuous Innovation at the National Center for Nanoscience and Technology of China (NCNST)
Bibliographic record
Abstract
This special issue of Advanced Materials celebrates the 20th anniversary of the National Center for Nanoscience and Technology of China (NCNST), showcasing a diverse array of cutting-edge research in nanoscience and nanotechnology.This issue highlights recent advances in nanomaterials for devices, nanomedicine, energy, and catalysis applications.NCNST is a pioneering institution established on December 31, 2003 and supported by the National Development and Reform Commission.NCNST is the first state-level hub of the nation, dedicated to nanoscience innovation.The collaboration between the Chinese Academy of Sciences (CAS) and the Ministry of Education has place NCNST at the forefront of doing research, fostering talents, and engaging international exchange in the field of nanoscience and nanotechnology.Guided by the visionary leadership of Prof. Chunli Bai, former CAS president, NCNST has firmly established itself as a forerunner in nanoscience and nanotechnology over the past two decades.In 2021, 5 researchers from NCNST were honored as "Highly Cited Researchers" by Clarivate.[1] To connect the various disciplines within nanoscience and nanotechnology, three high impact nanoscience journals, i.e.,
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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 | 0.007 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.013 | 0.005 |
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".