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Record W4389339050 · doi:10.1038/s41467-023-43594-4

Wood-based superblack

2023· article· en· W4389339050 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueNature Communications · 2023
Typearticle
Languageen
FieldEnergy
TopicSolar-Powered Water Purification Methods
Canadian institutionsUniversity of British Columbia
FundersHorizon 2020 Framework ProgrammeChina Scholarship CouncilCanada Foundation for InnovationAcademy of FinlandWalter Ahlströmin SäätiöAalto-YliopistoEuropean CommissionTekniikan EdistämissäätiöCanada Excellence Research Chairs, Government of Canada
KeywordsComputer science

Abstract

fetched live from OpenAlex

Light is a powerful and sustainable resource, but it can be detrimental to the performance and longevity of optical devices. Materials with near-zero light reflectance, i.e. superblack materials, are sought to improve the performance of several light-centered technologies. Here we report a simple top-down strategy, guided by computational methods, to develop robust superblack materials following metal-free wood delignification and carbonization (1500 °C). Subwavelength severed cells evolve under shrinkage stresses, yielding vertically aligned carbon microfiber arrays with a thickness of ~100 µm and light reflectance as low as 0.36% and independent of the incidence angle. The formation of such structures is rationalized based on delignification method, lignin content, carbonization temperature and wood density. Moreover, our measurements indicate a laser beam reflectivity lower than commercial light stoppers in current use. Overall, the wood-based superblack material is introduced as a mechanically robust surrogate for microfabricated carbon nanotube arrays.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.877
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0020.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.002

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 head0.059
GPT teacher head0.364
Teacher spread0.305 · 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