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Record W4226378009 · doi:10.1126/sciadv.abm8455

Using transient equilibria (TREQ) to measure the thermodynamics of slowly assembling supramolecular systems

2022· article· en· W4226378009 on OpenAlexaff
Christopher Hennecker, Christophe Lachance‐Brais, Hanadi F. Sleiman, Anthony Mittermaier

Bibliographic record

VenueScience Advances · 2022
Typearticle
Languageen
FieldMaterials Science
TopicSupramolecular Self-Assembly in Materials
Canadian institutionsMcGill University
Fundersnot available
KeywordsSupramolecular chemistryThermodynamicsCyanuric acidMeasure (data warehouse)Transient (computer programming)ChemistryStatistical physicsChemical physicsMoleculeComputer sciencePhysicsData mining

Abstract

fetched live from OpenAlex

Supramolecular chemistry involves the noncovalent assembly of monomers into materials with unique properties and wide-ranging applications. Thermal analysis is a key analytical tool in this field, as it provides quantitative thermodynamic information on both the structural stability and nature of the underlying molecular interactions. However, there exist many supramolecular systems whose kinetics are so slow that the thermodynamic methods currently applied are unreliable or fail completely. We have developed a simple and rapid spectroscopic method for extracting accurate thermodynamic parameters from these systems. It is based on repeatedly raising and lowering the temperature during assembly and identifying the points of transient equilibrium as they are passed on the up- and down-scans. In a proof-of-principle application to the coassembly of polydeoxyadenosine (polyA) containing 15 adenosines and cyanuric acid (CA), we found that roughly 30% of the CA binding sites on the polyA chains were unoccupied, with implications for high-valence 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 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)0.005
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 score0.109
Threshold uncertainty score0.812

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0020.001
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 head0.032
GPT teacher head0.304
Teacher spread0.272 · 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 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

Citations4
Published2022
Admission routes1
Has abstractyes

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