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Record W4403494618 · doi:10.26434/chemrxiv-2024-mv5wm

Gallium-catalyzed Boron-assisted Recycling of Any Silicone Waste: Depolymerization to produce Chlorosilanes as key industrial commodities

2024· preprint· en· W4403494618 on OpenAlexaff
Nam Đức Vũ, Aurélie Boulègue-Mondière, Nicolas Durand, Joséphine Munsch, Mickaël Boste, Rudy Lhermet, David Gajan, Anne Baudouin, Steven Roldán‐Gómez, Lionel Perrin, Vincent Monteil, Jean Raynaud

Bibliographic record

VenueChemRxiv · 2024
Typepreprint
Languageen
FieldChemistry
TopicOrganoboron and organosilicon chemistry
Canadian institutionsActivation Laboratories
FundersRégion Auvergne-Rhône-AlpesCentre National de la Recherche Scientifique
KeywordsSiliconeDepolymerizationCatalysisMaterials sciencePolymerBoronMonomerGalliumChemical engineeringOrganic chemistryPolymer chemistryChemistryComposite materialMetallurgyEngineering

Abstract

fetched live from OpenAlex

Chemical recycling back to monomers is a key strategy for a sustainable circular polymer economy. Silicone polymers and networks are wonder hybrid materials with a robust inorganic backbone and tunable organic substituents tailored for various daily life applications. However, their recycling, including mechanical and chemical processes, remains at its infancy. We present a generalized method to depolymerize, at ambient temperatures, any silicone waste; including a very wide range of silicone-based materials and post-consumer waste a.k.a. end-of-life crosslinked polydimethysiloxane-based networks within formulated materials. The reaction harnesses an efficient gallium catalyst, with a 30-million-fold rate enhancement, and boron trichloride as source of chloride to produce nearly quantitative yields of (methyl)chlorosilanes a.k.a. key intermediates from the Müller-Rochow process, at the cornerstone of the Si industry.

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)0.000
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 score0.003

Distilled classifier scores by category (both heads)

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 integrity0.0000.001
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 head0.030
GPT teacher head0.262
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 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

Citations1
Published2024
Admission routes1
Has abstractyes

Explore more

Same venueChemRxiv→Same topicOrganoboron and organosilicon chemistry→French-language works237,207→