MétaCan
Menu
Back to cohort
Record W4396834411 · doi:10.1002/adfm.202402894

Unveiling the Potential of Halide Perovskites for Seasonally Adaptive CO<sub>2</sub> Photoreduction under Low Light Conditions

2024· article· en· W4396834411 on OpenAlexaff
Naveen Kumar Tailor, Shreya Singh, Saurabh K. Saini, Kaivalya, Mohammad Adil Afroz, Mahesh Kumar, Sebastian C. Peter, Kamal Kishore Pant, Soumitra Satapathi

Bibliographic record

VenueAdvanced Functional Materials · 2024
Typearticle
Languageen
FieldEnergy
TopicAdvanced Photocatalysis Techniques
Canadian institutionsUniversity of Saskatchewan
FundersScience and Engineering Research BoardIndian Institute of Technology DelhiDepartment of Science and Technology, Ministry of Science and Technology, India
KeywordsHalideMaterials sciencePhotochemistryOptoelectronicsChemical physicsInorganic chemistryNanotechnologyPhysicsChemistry

Abstract

fetched live from OpenAlex

Abstract The viability of double perovskite Cs 2 AgBiBr 6 in low‐light environments is studied for CO 2 photoreduction. It is observed that light intensity significantly influences product formation with I 0.56 depending on the overall product formation rate. The photodetection measurement reveals that photocurrent as a function of incident power follows a power law ∝ I 0 α with α = 0.80, which is attributed to the carrier trapping at higher light power. Furthermore, power‐dependent transient absorption spectroscopy is studied and observed that at higher fluence, carrier scattering can be dominating. Interestingly, it is found that at higher power, hot carrier relaxation dynamics are altered, and electron‐phonon coupling is enhanced, resulting in a lower carrier concentration participating in the photocatalytic reaction, which can limit charge carrier extraction and CO 2 reduction. This study highlights the potential of perovskite semiconductors as promising candidates for photocatalytic reactions in low‐light conditions, broadening their applicability in real‐world scenarios.

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)0.000
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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.111
Threshold uncertainty score0.958

Codex and Gemma teacher scores by category

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 communication0.0000.001
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 head0.012
GPT teacher head0.262
Teacher spread0.250 · 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.

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

Citations11
Published2024
Admission routes1
Has abstractyes

Explore more

Same venueAdvanced Functional MaterialsSame topicAdvanced Photocatalysis TechniquesFrench-language works237,207