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Record W2324658664 · doi:10.1021/nn505705a

Size-Selected TiO<sub>2</sub> Nanocluster Catalysts for Efficient Photoelectrochemical Water Splitting

2014· article· en· W2324658664 on OpenAlexafffund
Saurabh Srivastava, Joseph P. Thomas, Md. Anisur Rahman, Marwa Abd‐Ellah, Mamata Mohapatra, Debabrata Pradhan, Nina F. Heinig, K. T. Leung

Bibliographic record

VenueACS Nano · 2014
Typearticle
Languageen
FieldEnergy
TopicAdvanced Photocatalysis Techniques
Canadian institutionsUniversity of Waterloo
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsWater splittingCatalysisMaterials sciencePhotocatalysisNanotechnologyPhotoelectrochemistryChemical engineeringElectrochemistryChemistryPhysical chemistryElectrode

Abstract

fetched live from OpenAlex

Nanoclusters (NCs) are of great interest because they provide the link between the distinct behavior of atoms and nanoparticles and that of bulk materials. Here, we report precisely controlled deposition of size-selected TiO2 NCs produced by gas-phase aggregation in a special magnetron sputtering system. Carefully optimized aggregation length and Ar gas flow are used to control the size distribution, while a quadrupole mass filter provides precise in situ size selection (from 2 to 15 nm). Transmission electron microscopy studies reveal that NCs larger than a critical size (∼8 nm) have a crystalline core with an amorphous shell, while those smaller than the critical size are all amorphous. The TiO2 NCs so produced exhibit remarkable photoelectrochemical water splitting performance in spite of a small amount of material loading. NCs of three different sizes (4, 6, and 8 nm) deposited on H-terminated Si(100) substrates are tested for the photoelectrochemical catalytic performance, and significant enhancement in photocurrent density (0.8 mA/cm(2)) with decreasing NC size is observed with a low saturation voltage of -0.22 V vs Ag/AgCl (0.78 V vs RHE). The enhanced photoconductivity could be attributed to the increase in the specific surface area and increase in the number of active (defect) sites in the amorphous NCs. The unique advantages of the present technique will be further exploited to develop applications based on tunable, size-selected NCs.

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 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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
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 score0.010
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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)0.0000.000

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 head0.005
GPT teacher head0.227
Teacher spread0.222 · 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 teacher head, not a consensus.

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

Citations84
Published2014
Admission routes2
Has abstractyes

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