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Record W3033009881 · doi:10.1039/d0sc02703k

Development of tethered dual catalysts: synergy between photo- and transition metal catalysts for enhanced catalysis

2020· article· en· W3033009881 on OpenAlexaff
Danfeng Wang, Robert Malmberg, Indrek Pernik, Shyamal K. K. Prasad, Max Roemer, Koushik Venkatesan, Timothy W. Schmidt, Sinead T. Keaveney, Barbara A. Messerle

Bibliographic record

VenueChemical Science · 2020
Typearticle
Languageen
FieldEnergy
TopicAdvanced Photocatalysis Techniques
Canadian institutionsKensington Health
FundersUniversity of New South WalesMacquarie UniversityAustralian SynchrotronCentre of Excellence in Exciton ScienceAustralian Nuclear Science and Technology Organisation
KeywordsCatalysisTransition metalPhotocatalysisReactivity (psychology)Synergistic catalysisChemistryDual (grammatical number)Dual roleCombinatorial chemistryPhotochemistryOrganic chemistry

Abstract

fetched live from OpenAlex

While dual photocatalysis-transition metal catalysis strategies are extensively reported, the majority of systems feature two separate catalysts, limiting the potential for synergistic interactions between the catalytic centres. In this work we synthesised a series of tethered dual catalysts allowing us to investigate this underexplored area of dual catalysis. In particular, Ir(i) or Ir(iii) complexes were tethered to a BODIPY photocatalyst through different tethering modes. Extensive characterisation, including transient absorption spectroscopy, cyclic voltammetry and X-ray absorption spectroscopy, suggest that there are synergistic interactions between the catalysts. The tethered dual catalysts were more effective at promoting photocatalytic oxidation and Ir-catalysed dihydroalkoxylation, relative to the un-tethered species, highlighting that increases in both photocatalysis and Ir catalysis can be achieved. The potential of these catalysts was further demonstrated through novel sequential reactivity, and through switchable reactivity that is controlled by external stimuli (heat or light).

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 score0.024
Threshold uncertainty score0.914

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
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 head0.024
GPT teacher head0.279
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

Citations25
Published2020
Admission routes1
Has abstractyes

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