Inorganic & Coordination Chemistry: Abstracts 211-279
Bibliographic record
Abstract
In an approach to design selectives olid catalysts we start fromt he knowledge,atthe molecularlevel,ofthe reactiontobecatalyzed.Thenhypothesisa re made on then atureo ft he actives itesr equired.A tt hisp oint we are readytosynthesizesolid materials, in where therequiredactivesitesare introduced as well definedentities.On topofthatthe adsorption propertiesof thesolid are taylored to optimizethe interactions between reactants, catalyst andproducts.Following this methodologyw ill presents olid catalysts in where thea ctive sitescorrespondtowelldefinedtransitionmetal complexesand organocatalysts thata re either graftedo rs tructurally builded into solids.In this case, ther oleo ft he solid can go beyond as imple support, since it is designed to interveneinthe reactioneither by stabilizing transitionstatesorbyintroducingadditionalactivesites.Well definedsingleormultiple activesitescan also be introduced into crystallinen anoporous materialsw ith controlleda dsorptionp roperties, andt his allows to perform newacidand redox,one step or multistepreactions.Finallyw ill show that by depositingm etal nanoparticles( Au, Pd,P t) on proactivesupports(CeO 2 ,Fe 2 O 3 ,MgO,hydrotalcites,etc.) we can open new catalytic reactionroutesf or C-Cbond formation, oxidations andreductions.These catalytic system allowt he design of multifunctionals olid catalysts, that are able to carryo ut multistepp rocess through cascadet yper eactions that were not possiblebefore.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.101 | 0.037 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".