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Record W3157783383 · doi:10.1021/acscatal.1c00613

Structurally-Responsive Ligands for High-Performance Catalysts

2021· article· en· W3157783383 on OpenAlex

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

VenueACS Catalysis · 2021
Typearticle
Languageen
FieldChemistry
TopicSynthetic Organic Chemistry Methods
Canadian institutionsWestern University
Fundersnot available
KeywordsCatalysisDenticityLigand (biochemistry)ChemistrySelectivityCombinatorial chemistryPerspective (graphical)NanotechnologyMaterials scienceComputer scienceMetalOrganic chemistryReceptorBiochemistry

Abstract

fetched live from OpenAlex

Hemilabile ligands that undergo reversible changes in denticity have long been exploited to enable catalyst activation and to increase lifetime. However, this common description does not fully reflect reality with respect to the diversity of known ligand types, modes of action, and impacts on catalysis. The term structurally responsive ligand (SRL) is employed here to encompass ligands that exhibit self-tuning denticity, hapticity, or versatile coordination. This Perspective covers case study examples of SRLs in catalysis and their influence on catalyst lifetime, rate, and selectivity. The examples include leading and emerging catalysts for enabling transformations, which emphasizes both the breadth and significance of this class of ligands.

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)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
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 score0.015
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.012
GPT teacher head0.257
Teacher spread0.245 · 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