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Record W4361196924 · doi:10.1002/anie.202302446

Diphosphoryl‐functionalized Polyoxometalates: Structurally and Electronically Tunable Hybrid Molecular Materials

2023· article· en· W4361196924 on OpenAlexfundno aff
Sharad S. Amin, Kieran D. Jones, Alexander J. Kibler, Heather A. Damian, Jamie M. Cameron, Kevin Butler, Stephen P. Argent, Max Winslow, David Robinson, Nicholas J. Mitchell, Hon Wai Lam, Graham N. Newton

Bibliographic record

VenueAngewandte Chemie International Edition · 2023
Typearticle
Languageen
FieldMaterials Science
TopicPolyoxometalates: Synthesis and Applications
Canadian institutionsnot available
FundersEngineering and Physical Sciences Research CouncilTrent UniversityNottingham Trent University
KeywordsPolyoxometalatePhosphonateChemistryHybrid materialHydrolysisRedoxCombinatorial chemistryStereochemistryPolymer chemistryCrystallographyOrganic chemistryCatalysis

Abstract

fetched live from OpenAlex

Abstract Herein, we report the synthesis and characterization of a new class of hybrid Wells–Dawson polyoxometalate (POM) containing a diphosphoryl group (P2O6X) of the general formula [P2W17O57(P2O6X)]6−(X=O, NH, or CR1R2). Modifying the bridging unit X was found to impact the redox potentials of the POM. The ease with which a range of α‐functionalized diphosphonic acids (X=CR1R2) can be prepared provides possibilities to access diverse functionalized hybrid POMs. Compared to existing phosphonate hybrid Wells–Dawson POMs, diphosphoryl‐substituted POMs offer a wider tunable redox window and enhanced hydrolytic stability. This study provides a basis for the rational design and synthesis of next‐generation hybrid Wells–Dawson POMs.

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 score0.004

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 integrity0.0000.000
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 head0.010
GPT teacher head0.245
Teacher spread0.236 · 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

Citations18
Published2023
Admission routes1
Has abstractyes

Explore more

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