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Record W3002951090 · doi:10.1039/c9sc05640h

Efficiently self-healing boronic ester crystals

2020· article· en· W3002951090 on OpenAlexfundno aff
Patrick Commins, Marieh B. Al‐Handawi, Durga Prasad Karothu, Gijo Raj, Pancě Naumov

Bibliographic record

VenueChemical Science · 2020
Typearticle
Languageen
FieldMaterials Science
TopicPolymer composites and self-healing
Canadian institutionsnot available
FundersYork UniversityNew York University Abu Dhabi
KeywordsBent molecular geometryCrystal (programming language)Materials scienceChemistryCrystallographyNanotechnologyStereochemistryComposite materialComputer science

Abstract

fetched live from OpenAlex

The perception of organic crystals being rigid static entities is quickly eroding, and molecular crystals are now matching a number of properties previously thought to be unique to soft materials. Here, we present crystals of a boronate ester that encompass many of the elastic and plastic mechanical properties of polymers such as bending, twisting, coiling and highly efficient self-healing of up to 67%, while they maintain their long-range structural order. The approach utilizes the concept of dynamic covalent chemistry and proves it can be applied towards ordered materials. This work expands our current understanding of the properties of crystalline molecular materials, and it could have implications towards the development of mechanically robust organic crystals that are capable of self-repair for durable all-organic electronics and soft robotics.

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)0.001
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 score0.031
Threshold uncertainty score0.598

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.016
GPT teacher head0.247
Teacher spread0.232 · 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

Citations79
Published2020
Admission routes1
Has abstractyes

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