MétaCan
Menu
Back to cohort
Record W3154797408 · doi:10.1002/cssc.202100624

Mechanoenzymology: State of the Art and Challenges towards Highly Sustainable Biocatalysis

2021· article· en· W3154797408 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

VenueChemSusChem · 2021
Typearticle
Languageen
FieldMaterials Science
TopicEnzyme Structure and Function
Canadian institutionsMcGill University
Fundersnot available
KeywordsBiochemical engineeringGreen chemistryNanotechnologyMechanochemistryBiocatalysisConceptualizationOrganocatalysisSynthetic biologyChemistryComputer scienceCatalysisMaterials scienceOrganic chemistryEngineeringReaction mechanismBiology

Abstract

fetched live from OpenAlex

Global awareness of the importance of developing environmentally friendlier and more sustainable methods for the synthesis of valuable chemical compounds has led to the design of novel synthetic strategies, involving bio- and organocatalysis as well as the application of novel efficient and ground-breaking technologies such as present-day solvent-free mechanochemistry. In this regard, the evaluation of biocatalytic protocols mediated by the combination of mechanical activation and enzymatic catalysis has recently attracted the attention of the chemical community. Such mechanoenzymatic strategy represents an innovative and promising "green" approach in chemical synthesis that poses nevertheless new paradigms regarding the relative resilience of biomolecules to the mechanochemical stress and to the apparent high energy, at least in so-called hot-spots, during the milling process. Herein, relevant comments on the conceptualization of such mechanoenzymatic approach as a sustainable option in chemical synthesis, recent progress in the area, and associated challenges are discussed.

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)0.000
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 score0.020
Threshold uncertainty score0.295

Codex and Gemma teacher scores by category

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

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 head0.014
GPT teacher head0.210
Teacher spread0.196 · 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