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Record W3005617961 · doi:10.1002/smll.201906567

Ultrasensitive Physical, Bio, and Chemical Sensors Derived from 1‐, 2‐, and 3‐D Nanocellulosic Materials

2020· review· en· W3005617961 on OpenAlex

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.

Bibliographic record

VenueSmall · 2020
Typereview
Languageen
FieldMaterials Science
TopicAdvanced Cellulose Research Studies
Canadian institutionsUniversity of New BrunswickFPInnovations
FundersNational Key Research and Development Program of ChinaChina Postdoctoral Science FoundationNatural Science Foundation of Shaanxi ProvinceNational Natural Science Foundation of China
KeywordsNanocelluloseNanotechnologyNanofiberBacterial celluloseCelluloseNanomaterialsContext (archaeology)Materials scienceChemical engineeringEngineeringBiology

Abstract

fetched live from OpenAlex

Sensors are of increasing interest since they can be applied to daily life in different areas from various industrial sectors. As a natural nanomaterial, nanocellulose plays a vital role in the development of novel sensors, particularly in the context of constructing multidimensional architectures. This review summarizes the utilization of nanocellulose including cellulose nanofibers, cellulose nanocrystals, and bacterial cellulose for sensor design, mainly focusing on the influence of nanocellulose on the sensing performance of these sensors. Special attention is paid to nanocellulose in different forms (1D, 2D, and 3D) to highlight the impact of nanocellulose constructed structures. The aim is to provide a critical review on the most recent progress (especially after 2017) related to nanocellulose-containing sensors, since there are significantly increasing research activities in this area. Moreover, the outlook for the development of nanocellulose-containing sensors is also provided at the end of this work.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.232
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.001
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 head0.045
GPT teacher head0.307
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