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Record W3184118031 · doi:10.3390/molecules26154585

Photochemical Synthesis of Gold and Silver Nanoparticles—A Review

2021· review· en· W3184118031 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueMolecules · 2021
Typereview
Languageen
FieldMaterials Science
TopicNanoparticles: synthesis and applications
Canadian institutionsPolytechnique Montréal
Fundersnot available
KeywordsNanotechnologyNanomaterialsReagentNanoparticleColloidal goldCatalysisMaterials scienceNoble metalChemistryOrganic chemistry

Abstract

fetched live from OpenAlex

Nanomaterials have supported important technological advances due to their unique properties and their applicability in various fields, such as biomedicine, catalysis, environment, energy, and electronics. This has triggered a tremendous increase in their demand. In turn, materials scientists have sought facile methods to produce nanomaterials of desired features, i.e., morphology, composition, colloidal stability, and surface chemistry, as these determine the targeted application. The advent of photoprocesses has enabled the easy, fast, scalable, and cost- and energy-effective production of metallic nanoparticles of controlled properties without the use of harmful reagents or sophisticated equipment. Herein, we overview the synthesis of gold and silver nanoparticles via photochemical routes. We extensively discuss the effect of varying the experimental parameters, such as the pH, exposure time, and source of irradiation, the use or not of reductants and surfactants, reagents' nature and concentration, on the outcomes of these noble nanoparticles, namely, their size, shape, and colloidal stability. The hypothetical mechanisms that govern these green processes are discussed whenever available. Finally, we mention their applications and insights for future developments.

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.

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: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.621
Threshold uncertainty score0.763

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.032
GPT teacher head0.306
Teacher spread0.274 · 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