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Record W3095134366 · doi:10.1002/smll.202070233

Adhesive Coacervates: Adhesive Coacervates Driven by Hydrogen‐Bonding Interaction (Small 43/2020)

2020· article· en· W3095134366 on OpenAlexaff
Qiongyao Peng, Jingsi Chen, Zicheng Zeng, Tao Wang, Li Xiang, Xuwen Peng, Jifang Liu, Hongbo Zeng

Bibliographic record

VenueSmall · 2020
Typearticle
Languageen
FieldMedicine
TopicSurgical Sutures and Adhesives
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsCoacervateAdhesiveHydrogen bondMaterials sciencePolyethylene glycolSilicotungstic acidMembraneChemical engineeringPolymer chemistryPolymer scienceNanotechnologyChemistryOrganic chemistryMoleculeLayer (electronics)

Abstract

fetched live from OpenAlex

In article number 2004132, Hongbo Zeng and co-workers discover a novel coacervation phenomenon by mixing silicotungstic acid and nonionic polyethylene glycol in water, driven by hydrogen bonding interactions, which provides a new platform for engineering multifunctional coacervate materials as tissue glues, wound dressings and membrane-free cell systems.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.003
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.002

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 head0.038
GPT teacher head0.266
Teacher spread0.228 · 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 source (direct Gemma or distilled Codex), 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

Citations1
Published2020
Admission routes1
Has abstractyes

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