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Record W4322771609 · doi:10.1016/j.isci.2023.106317

Nanostructured silicon photocatalysts for solar-driven fuel production

2023· review· en· W4322771609 on OpenAlex
Sarrah Putwa, Isabel S. Curtis, Mita Dasog

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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueiScience · 2023
Typereview
Languageen
FieldEnergy
TopicAdvanced Photocatalysis Techniques
Canadian institutionsDalhousie University
FundersKillam Trusts
KeywordsSolar fuelNanotechnologyHydrogen productionPhotocatalysisSiliconMaterials scienceNanostructureWater splittingSolar energyHydrogenCatalysisChemistryEngineering

Abstract

fetched live from OpenAlex

Solar-driven production of fuels such as hydrogen, hydrocarbons, and ammonia using semiconducting photocatalysts has the potential to be a sustainable alternative to current chemical processes. In recent years, silicon (Si) nanostructures have been recognized as a promising photocatalyst for hydrogen generation and organic oxidation reactions owing to its abundance, biocompatibility, and cost. While bulk Si has been studied extensively, on the nanoscale, plenty of opportunities exist to understand and engineer optimally performing Si photocatalysts. This perspective will highlight key results on the use of Si nanostructures for photocatalytic H 2 production, CO 2 reduction via light and heat-driven chemical looping, and current challenges in utilizing it for fuel-forming reactions. A brief guide on how these challenges can be addressed in the future and other unexplored questions that remain in the field are also discussed.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.988
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.060
GPT teacher head0.362
Teacher spread0.302 · 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