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Record W4308323132 · doi:10.1016/j.cej.2022.140225

State-of-the-art luminescent materials based on wood veneer with superior strength, transparency, and water resistance

2022· article· en· W4308323132 on OpenAlexafffund
Ming Wang, Hongchen Liu, Xiao Feng, Xijun Wang, Kaiyuan Shen, Haisong Qi, Orlando J. Rojas

Bibliographic record

VenueChemical Engineering Journal · 2022
Typearticle
Languageen
FieldMaterials Science
TopicLuminescence and Fluorescent Materials
Canadian institutionsUniversity of British Columbia
FundersJiangnan UniversityBasic and Applied Basic Research Foundation of Guangdong ProvinceCanada Excellence Research Chairs, Government of CanadaEuropean Commission
KeywordsVeneerTransparency (behavior)Water resistanceMaterials scienceComposite materialLuminescenceComputer scienceOptoelectronicsComputer security

Abstract

fetched live from OpenAlex

Current energy and luminescent materials utilize polymers to encapsulate fluorescent carbon nanodots and dyes but they usually lack stability and biodegradability. Here, we introduce an alternative wood nanotechnology to produce luminescent films (veneer) with UV resistance and tailorable surface energy. First, fluorescent and transparent wood (FTW) is shown as a support of aggregation-induced fluorescent emission (AIE) by in-situ Hantzsch reaction of acetoacetate delignified wood (AA-DW). Highly aligned cellulose nanofibers present in AA-DW facilitate FTW densification, ensuing exceptional mechanical performance (tensile strength and Young's modulus of up to 422 MPa and 45 GPa, respectively). Simultaneously, the conjugated structures display high transparency (83 %), haze (80 %) and optical anisotropy. The new luminescent system is demonstrated to be biodegradable and effective in optical (fluorescent) windows as well as encrypted films.

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 score0.002
Threshold uncertainty score0.783

Codex and Gemma teacher scores by category

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)0.0010.000

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 head0.005
GPT teacher head0.176
Teacher spread0.171 · 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

Citations22
Published2022
Admission routes2
Has abstractyes

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