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Record W4246166836 · doi:10.26434/chemrxiv.14398856

Low-Power Laser Micro-Shaping of Dye-Volatile Cocrystals: The Gentle Cutting Edge of Photoresponsive Materials

2021· preprint· en· W4246166836 on OpenAlexafffund
Tristan H. Borchers, Filip Topić, Jan-Constantin Christopherson, Oleksandr S. Bushuyev, Jogirdas Vainauskas, Hatem M. Titi, Tomislav Friščić, Christopher J. Barrett

Bibliographic record

VenueChemRxiv · 2021
Typepreprint
Languageen
FieldEngineering
TopicLaser Material Processing Techniques
Canadian institutionsMcGill University
FundersWestern Canada Research GridCompute CanadaNatural Sciences and Engineering Research Council of CanadaMcGill University
KeywordsCocrystalMaterials scienceLaserPolymerVisible spectrumCeramicLaser power scalingOptoelectronicsPhotochemistryChemical engineeringOpticsChemistryComposite materialMoleculeOrganic chemistry

Abstract

fetched live from OpenAlex

Cocrystallisation of a fluorinated azobenzene with volatile cocrystal components dioxane or pyrazine yields halogen-bonded cocrystals that can be cut, carved or engraved with low-powered visible laser light. This process, termed cocrystal laser micro-shaping (CLMS), is enabled by cocrystallisation of a visible light dye with a volatile component, giving rise to materials that can be selectively disassembled with micrometer precision using gentle, non-burning irradiation in a commercial confocal microscope setup. The ability to shape and even machine cocrystals in 3D using laser powers between 0.5 and 20 mW, which are 2-4 orders of magnitude lower compared to laser powers used for machining metals, ceramics or polymers, is rationalized by CLMS targeting the disruption of weak supramolecular interactions between cocrystal components, rather than the breaking of covalent bonds in polymers or disruption of ionic structures required for conventional laser beam or focused ion beam machining processes, mainly by high-power laser heating.<br>

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 categoriesMeta-epidemiology (narrow)
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 score0.003
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.017
GPT teacher head0.242
Teacher spread0.225 · 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.

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

Citations0
Published2021
Admission routes2
Has abstractyes

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