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Record W4402516268 · doi:10.1002/slct.202401953

Ultrasound‐Assisted Catalysis: A Pathway to Novel and Selective Chemical Transformations in Condensed Phase

2024· article· en· W4402516268 on OpenAlexafffund
Shang Jiang, Evelyne Kafui Yawa Late, F. Jérôme, Prince Nana Amaniampong, Samir H. Mushrif

Bibliographic record

VenueChemistrySelect · 2024
Typearticle
Languageen
FieldMaterials Science
TopicUltrasound and Cavitation Phenomena
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of CanadaUniversité de PoitiersCentre National de la Recherche ScientifiqueAgence Nationale de la RechercheUniversity of Alberta
KeywordsCatalysisSonochemistryChemical reactionChemistryChemical processHeterogeneous catalysisImplosionCavitationPhase (matter)RadicalChemical physicsMass transferNanotechnologyChemical engineeringMaterials scienceOrganic chemistryThermodynamicsPhysics

Abstract

fetched live from OpenAlex

Abstract In this paper, we review the concept of synergistically using ultrasonic sonochemistry in combination with heterogeneous catalysis, to selectively perform condensed phase chemical transformations. Through specific examples, we highlight that ultrasound induced cavitation bubbles not only provide local high temperature and pressure conditions for initiating chemical reactions but also improves heat, mass and charge transfer characteristics in the system. We also show that sonocatalysis can alter reaction mechanisms and open new activation and reaction pathways that are not intuitive to thermocatalysis. Computational chemistry tools are introduced and are combined with sonocatalysis experimental studies to demonstrate that, in‐situ generated radicals propelled into the bulk liquid solution upon bubbles implosion can be stabilized by catalyst surfaces, while creating active sites for unique reactions during heterogeneous sonocatalytic chemistry. We also report recent examples of condensed phase sonocatalysis applications, including pollutant degradation, chemical and pharmaceutical synthesis and biomass upgrading, and provide mechanistic insights into the process.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.658

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.012
GPT teacher head0.267
Teacher spread0.255 · 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
Published2024
Admission routes2
Has abstractyes

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