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Record W2960222646 · doi:10.1039/c9nr04348a

Recent progress in black phosphorus and black-phosphorus-analogue materials: properties, synthesis and applications

2019· review· en· W2960222646 on OpenAlexaff
Yi‐Jun Xu, Zhe Shi, Xinyao Shi, Kai Zhang, Han Zhang

Bibliographic record

VenueNanoscale · 2019
Typereview
Languageen
FieldMaterials Science
Topic2D Materials and Applications
Canadian institutionsL'Alliance Boviteq
FundersChina Postdoctoral Science FoundationNatural Science Foundation of Guangdong ProvinceNational Natural Science Foundation of China
KeywordsBlack phosphorusPhosphorusMaterials scienceNanotechnologyMetallurgyOptoelectronics

Abstract

fetched live from OpenAlex

Black phosphorus (BP), a novel two-dimensional (2D) layered semiconductor material, has attracted tremendous attention since 2014 due to its prominent carrier mobility, thickness-dependent direct bandgap and in-plane anisotropic physical properties. BP has been considered as a promising material for many applications, such as in transistors, photonics, optoelectronics, sensors, batteries and catalysis. However, the development of BP was hampered by its instability under ambient conditions, as well as by the lack of methods to synthesize large-area and high quality 2D nanofilms. Recently, some BP-analogue materials such as binary phosphides (MPx), transition metal phosphorus trichalcogenides (MPX3), and 2D group V (pnictogens) and 2D group VI materials have attracted increasing interest for their unique and stable structures, and excellent physical and chemical properties. This article, which focuses on BP and BP-analogue materials, will present their crystal structure, properties, synthesis methods and applications. Also the similarity and difference between BP and BP-analogue materials will be discussed, and the presentation of the future opportunities and challenges of the materials are included at the end.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.049
GPT teacher head0.295
Teacher spread0.246 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations336
Published2019
Admission routes1
Has abstractyes

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