MétaCan
Menu
Back to cohort
Record W4410289538 · doi:10.1021/acsomega.4c10337

Optimal Wavelengths and Solvents to Modulate Diazirine Kinetics

2025· article· en· W4410289538 on OpenAlexaff
Elwin W. J. Ang, Ivan Djordjevic, Animesh Ghosh, Chen Yee Goh, Han Zhang, Bryce Yu Heng Leck, Miranda J. Baran, Jeremy E. Wulff, Terry W. J. Steele

Bibliographic record

VenueACS Omega · 2025
Typearticle
Languageen
FieldChemistry
TopicChemical Reactions and Mechanisms
Canadian institutionsUniversity of Victoria
FundersMinistry of Education - SingaporeNational Research Foundation SingaporeSingapore's National Water AgencyAgency for Science, Technology and Research
KeywordsDiazirineKineticsWavelengthChemistryPhysicsPhotochemistryOpticsClassical mechanics

Abstract

fetched live from OpenAlex

Diazirine photolysis generates reactive carbene species, useful for indiscriminate cross-linking and insertion reactions with amino acids and polyolefins. However, methods to increase the carbene efficiency and limit the benign diazoalkane intermediate remain uncharted. Herein, we correlate diazirine depletion to common experimental parameters such as LED wavelength, temperature, solvent, and grafted functional groups. Broad wavelength activation impeded the formation of diazoalkane, presenting a light-based method to limit this yellow-inducing intermediate. Kinetics rate (per joule) values of diazirine depletion and diazoalkane generation served as the performance metric, permitting comparisons across all parameter variables. Photokinetics were independent of temperature from 25 to 70 °C. Highest rates were found in ester-containing solvents and ether conjugated diazirine. Overall, the novel irradiation arrangement reduces the reliance on UVA for photoactivation and increases the efficiency of diazirine as a carbene precursor.

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)0.000
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 score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.008
GPT teacher head0.252
Teacher spread0.244 · 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

Citations2
Published2025
Admission routes1
Has abstractyes

Explore more

Same venueACS OmegaSame topicChemical Reactions and MechanismsFrench-language works237,207