Silver Nanoparticles in Organic Transformations
Bibliographic record
Abstract
Silver is a versatile element, differing from gold in that only half of its supplies is used toward jewelry, the rest being geared toward industrial applications, including alloys, batteries, dentistry, glass coatings, LED chips, medicine, nuclear reactors, photography, photovoltaic energy, tracking chips, semiconductors, touch screens, water purification, wood preservatives, and many other uses. This chapter covers the catalytic processes catalyzed by silver nanoparticles (Ag NPs), with a special interest in the scope and mechanism of these reactions. It presents a survey with alkynylation, oxidation couplings, and miscellaneous processes toward nitrile hydrolysis, silanol chemistry, and Lewis acid catalysis. The chapter discusses the silver-catalyzed epoxidation of simple alkenes such as ethy-lene and propylene, using oxygen as an oxidant. It describes selected examples of both proposed pathways for this reaction: the aerobic oxidation and the dehydrogenation. The chapter examines the reduction of nitroarenes using hydrogen gas and also discusses Ag NP-catalyzed reactions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.385 | 0.007 |
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; both teacher heads agree on what is shown here.
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".