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Record W4415060351 · doi:10.26599/nr.2025.94908099

Surface micro and nano materials: Twenty years of innovation, collaboration, and translation at Xuchang University

2025· article· en· W4415060351 on OpenAlexaboutno aff
Weiwei He, Guangshan Zhu, Zhi Zheng

Bibliographic record

VenueNano Research · 2025
Typearticle
Languageen
FieldChemistry
TopicAerogels and thermal insulation
Canadian institutionsnot available
Fundersnot available
KeywordsNano-Surface (topology)Translation (biology)Surface structure

Abstract

fetched live from OpenAlex

we are delighted to celebrate the 20th anniversary of the Institute of Surface Micro and Nano Materials (ISMNM) at Xuchang University-a milestone that marks two decades of pioneering research, interdisciplinary collaboration, and impactful translation in the field of micro and nano materials.This special issue, featuring 94 cutting-edge papers from global researchers, not only honors the institute's legacy but also reflects the vibrant, rapidly evolving landscape of surface micro and nano materials science and engineering.Since its establishment in November 1 st , 2005, ISMNM has emerged as a cornerstone of nano research in Henan, China and beyond.From its early days as one of the nation's pioneering institutions dedicated to nanoscience, the institute has continuously expanded its horizons: establishing international joint laboratories with Canada's University of Western Ontario (2007) and Shanghai JiaoTong University (2008), Key Laboratory of Micro-Nano Energy Storage and Conversion Materials of Henan Province (2012), International Joint Laboratory of Nano Energy and Catalytic Materials of Henan Province (2017), and integrating with interdisciplinary academic units to form the College of Chemical and Materials Engineering (2020).In past ten years, ISMNM has been awarded 20+ national and provincial-level teaching and technological achievement awards.These milestones are not mere markers of growth-They embody ISMNM's core mission: to bridge fundamental nanoscience with real-world challenges in energy, environment, and life health.Over 20 years, ISMNM has built a dynamic ecosystem of innovation.Its team of 86 researchers-including 11 professors, 22 associate professors, and 76 PhDs-has been joined by 30+ scholars holding provincial and national honors, supported by 5 elite provincial teams (including a Huang Danian-style Teacher Team).Guided by an academic committee led by Academician Junhao Chu (Chinese Academy of Sciences) and featuring 20+ renowned experts, the institute has fostered an environment of "openness, freedom, collaboration, and inclusivity"-a ethos that has attracted

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.

How this classification was reachedexpand

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.221

Codex and Gemma teacher scores by category

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

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 head0.048
GPT teacher head0.329
Teacher spread0.282 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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