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Record W3112087041 · doi:10.33137/juls.v14i1.35210

Ketene and Ammonia Forming Acetamide in the Interstellar Medium

2020· article· en· W3112087041 on OpenAlexafffundvenue
Akash Kothari, Linglan Zhu, Jon Babi, Natalie J. Galant, Anita Rágyanszki, Imre G. Csizmadia

Bibliographic record

VenueJournal of Undergraduate Life Sciences · 2020
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAstrophysics and Star Formation Studies
Canadian institutionsYork UniversityUniversity of Toronto
FundersCompute Canada
KeywordsKeteneAcetamideInterstellar mediumAmmoniaChemistryAstrobiologyAstrophysicsPhysicsGalaxyOrganic chemistry

Abstract

fetched live from OpenAlex

Background: Peptide bonds are among the fundamental building blocks of life, polymerizing amino acids to form proteins that make up the structural components of living cells and regulate biochemical processes. The detection of glycine by NASA in comet Wild 2 in 2009 suggests the possibility of the formation of biomolecules in extraterrestrial environments through the interstellar medium. Detected in the dense molecular cloud Sagittarius B2, acetamide is the largest molecule containing a peptide bond and is hypothesized to be the precursor to all amino acids; as such, viability of its formation is of important biological relevance.
 Methods: Under a proposed mechanism of ammonia and ketene reactants, which have also been detected in dense molecular clouds in the ISM, the reaction pathway for the formation of acetamide was modelled using quantum chemical calculations in Gaussian16, using Austin-Frisch-Petersson functional with dispersion density functional theory at a 6-31G(d) basis set level of theory to optimize geometries and determine the thermodynamic properties for the reaction. Stability of the reactants, transition states, and products were examined to establish a reasonable mechanism.
 Conclusion: Product formation of acetamide was found to be highly exergonic and exothermic with a low energy barrier, suggesting a mechanism that is viable in the extreme density and temperature conditions found in ISM.

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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.407
Threshold uncertainty score0.165

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 head0.029
GPT teacher head0.263
Teacher spread0.234 · 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 designTheoretical or conceptual
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

Citations5
Published2020
Admission routes3
Has abstractyes

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