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Record W4366083437 · doi:10.1002/anie.202302286

Visible Light‐Driven Conversion of Carbon‐Sequestrated Seawater into Stoichiometric CO and HClO with Nitrogen‐Doped BiOCl Atomic Layers

2023· article· en· W4366083437 on OpenAlexaff
Yanbiao Shi, Hongwei Shou, Hao Li, Guangming Zhan, Xupeng Liu, Zhiping Yang, Chengliang Mao, Jundi Cheng, Xu Zhang, Yaqian Jiang, Shengxi Zhao, Jiaxian Wang, Xiao Liu, Li Song, Hongwei Sun, Lizhi Zhang

Bibliographic record

VenueAngewandte Chemie International Edition · 2023
Typearticle
Languageen
FieldEnergy
TopicAdvanced Photocatalysis Techniques
Canadian institutionsUniversity of Toronto
FundersKey Technologies Research and Development ProgramPostdoctoral Research Foundation of ChinaNational Natural Science Foundation of China
KeywordsSeawaterChemistryNitrogenChlorineStoichiometryInorganic chemistryCarbon fibersEnvironmental chemistryHalogenMaterials scienceAlkylOrganic chemistryGeologyOceanography

Abstract

fetched live from OpenAlex

Abstract Seawater is one of the most important CO 2 sequestration media for delivering value‐added chemicals/fuels and active chlorine; however, this scenario is plagued by sluggish reaction rates and poor product selectivity. Herein, we first report the synthesis of nitrogen‐doped BiOCl atomic layers to directly split carbon‐sequestrated natural seawater (Yellow Sea, China) into stoichiometric CO (92.8 μmol h −1 ) and HClO (83.2 μmol h −1 ) under visible light with selectivities greater than 90 %. Photoelectrons enriched on the exposed BiOCl{001} facet kinetically facilitate CO 2 ‐to‐CO reduction via surface‐doped nitrogen bearing Lewis basicity. Photoholes, mainly located on the lateral facets of van der Waals gaps, promote the selective oxidation of Cl − into HClO. Sequestrated CO 2 also maintains the pH of seawater at around 4.2 to prevent the alkaline earth cations from precipitating. The produced HClO can effectively kill typical bacteria in the ballast water of ocean‐going cargo ships, offering a green and safe way for onsite sterilization.

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 score0.009
Threshold uncertainty score0.646

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
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 head0.012
GPT teacher head0.269
Teacher spread0.257 · 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

Citations95
Published2023
Admission routes1
Has abstractyes

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