MétaCan
Menu
Back to cohort
Record W4396223646 · doi:10.1002/ifm2.12

Two‐dimensional germanium for photocatalysis

2024· article· en· W4396223646 on OpenAlexaff
Chengcheng Zhang, Guanshu Zhao, Dake Zhang, Shenghua Wang, Wei Sun

Bibliographic record

VenueInformation & Functional Materials · 2024
Typearticle
Languageen
FieldMaterials Science
TopicGraphene research and applications
Canadian institutionsUniversity of Toronto
FundersFundamental Research Funds for the Central UniversitiesNational Key Research and Development Program of China
KeywordsGermaniumSilicenePhotocatalysisSemiconductorMaterials scienceNanotechnologyGrapheneSemiconductor materialsBand gapEngineering physicsOptoelectronicsCatalysisPhysicsChemistrySilicon

Abstract

fetched live from OpenAlex

Abstract Succeeding graphene a series of two‐dimensional materials (2D M) have been developed and applied in various fields. As an analog of silicene, 2D germanium (2D Ge) has garnered vast attention owing to its novel structures and prolific properties, demonstrating substantial promise in semiconductor, catalysis, devices, and other burgeoning fields. Specifically, 2D Ge is advantageous in providing a massive specific surface area, preferable transport properties, a tunable band gap structure, and confinement effects. Endowed with unique features, functionalized 2D Ge has become a competitive candidate for photocatalysis. In this review, we catalog various synthetic methods of 2D Ge, discuss its fundamental properties, and summarize recent applications. We also present a few perspectives to provide fresh insights into designing and exploring 2D germanium in future photocatalysis.

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: none
Teacher disagreement score0.003
Threshold uncertainty score0.008

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)0.0030.001

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 head0.021
GPT teacher head0.282
Teacher spread0.262 · 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

Citations11
Published2024
Admission routes1
Has abstractyes

Explore more

Same venueInformation & Functional MaterialsSame topicGraphene research and applicationsFrench-language works237,207