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Record W2322358276 · doi:10.1002/cssc.201600088

Cadmium–Aluminum Layered Double Hydroxide Microspheres for Photocatalytic CO<sub>2</sub> Reduction

2016· article· en· W2322358276 on OpenAlexfundno aff
Daniel Saliba, Alaa Ezzeddine, Rachid Sougrat, Niveen M. Khashab, Mohamad Hmadeh, Mazen Al‐Ghoul

Bibliographic record

VenueChemSusChem · 2016
Typearticle
Languageen
FieldMaterials Science
TopicLayered Double Hydroxides Synthesis and Applications
Canadian institutionsnot available
FundersMinisterium für Wirtschaft, Innovation, Digitalisierung und Energie des Landes Nordrhein-WestfalenUniversity of Toronto
KeywordsHydroxidePhotocatalysisMaterials scienceMicrostructureCadmiumChemical engineeringInorganic chemistryScanning electron microscopeAluminiumPorosityMatrix (chemical analysis)CatalysisChemistryComposite materialMetallurgyOrganic chemistry

Abstract

fetched live from OpenAlex

We report the synthesis of cadmium-aluminum layered double hydroxide (CdAl LDH) using the reaction-diffusion framework. As the hydroxide anions diffuse into an agar gel matrix containing the mixture of aluminum and cadmium salts at a given ratio, they react to give the LDH. The LDH self-assembles inside the pores of the gel matrix into a unique spherical-porous shaped microstructure. The internal and external morphologies of the particles are studied by electron microscopy and tomography revealing interconnected channels and a high surface area. This material is shown to exhibit a promising performance in the photoreduction of carbon dioxide using solar light. Moreover, the palladium-decorated version shows a significant improvement in its reduction potential at room temperature.

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 score0.002

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 head0.024
GPT teacher head0.261
Teacher spread0.237 · 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

Citations39
Published2016
Admission routes1
Has abstractyes

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