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Record W2606293512 · doi:10.1039/c7nr01577a

Group III nitride nanomaterials for biosensing

2017· review· en· W2606293512 on OpenAlex
Li Xiao, Xinyu Liu

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

VenueNanoscale · 2017
Typereview
Languageen
FieldMaterials Science
TopicMXene and MAX Phase Materials
Canadian institutionsMcGill University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsBiosensorNanotechnologyNanomaterialsMaterials scienceNitride

Abstract

fetched live from OpenAlex

Biosensing has found wide applications in biological and medical research, and in clinical diagnosis, environmental monitoring and other analytical tasks. Recognized as novel and outstanding transducing materials because of their superior and unique physical/chemical properties, group III nitride (III-nitride) nanomaterials have been introduced into biosensor development with remarkable advancements achieved in the past few decades. This paper presents the first comprehensive review on biosensor development with III-nitride nanomaterials. The review starts with the introduction of the material properties and biocompatibility of III-nitrides that are useful for biosensing. The focus is then placed on surface treatments of III-nitrides, which lay the foundation for biosensing, and on biosensing mechanisms where the exceptional properties of III-nitride nanomaterials lead to superior biosensing performance. From a practical point of view, techniques for biosensor fabrication are then summarized. Finally, existing biosensing applications and future directions are 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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.906
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.136
GPT teacher head0.389
Teacher spread0.252 · 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