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Record W4323352760 · doi:10.1093/nsr/nwad062

Functional mesoporous materials in clean energy: an interview with Dongyuan Zhao

2023· article· en· W4323352760 on OpenAlex
He Zhu

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueNational Science Review · 2023
Typearticle
Languageen
FieldMaterials Science
TopicCovalent Organic Framework Applications
Canadian institutionsSR Research (Canada)
Fundersnot available
KeywordsMesoporous materialChinese academy of sciencesMolecular sieveNanotechnologyLibrary scienceMesoscopic physicsEngineering ethicsMaterials scienceChemical engineeringChemistryEngineeringComputer scienceOrganic chemistryPolitical sciencePhysicsChinaCatalysis

Abstract

fetched live from OpenAlex

invited Prof. Dongyuan Zhao of Fudan University for an interview focusing on his team's renowned research on functional mesoporous materials and energy-related applications. Prof. Zhao is a professor of chemistry and materials science, and a member of the Chinese Academy of Sciences. He received his PhD in chemistry from Jilin University in 1990. He has since focused his research on the synthesis and structure of porous materials and molecular sieves. His team received a first-tier national science award in 2021 for their contribution to the research and development of mesoscopic materials. They discovered a method of synthesizing mesoporous organic polymers and carbonaceous materials using organic-organic self-assembly. This work was published in 2005 and since then it has turned into a vibrant new field of more than 40 000 publications so far. His team has named more than 20 of their inventions after Fudan University: the FDU mesoporous series.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.362
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.003
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.001

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.

Opus teacher head0.057
GPT teacher head0.329
Teacher spread0.272 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it