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Record W4406803202 · doi:10.1002/lpor.202401839

Exceptional Absorption in a Deep‐Subwavelength Plasmonic Film

2025· article· en· W4406803202 on OpenAlexaff
Yiyun Zhang, Yiming Feng, Dominic Lepage, Bingtao Gao, Sihan Zhao, Yaoguang Ma, Yichen Shen, Shilong Li, Haoliang Qian

Bibliographic record

VenueLaser & Photonics Review · 2025
Typearticle
Languageen
FieldEngineering
TopicPlasmonic and Surface Plasmon Research
Canadian institutionsUniversité de Sherbrooke
FundersNational Key Research and Development Program of ChinaZhejiang UniversityNational Natural Science Foundation of China
KeywordsPlasmonAbsorption (acoustics)Materials scienceOptoelectronicsOpticsNanotechnologyPhysics

Abstract

fetched live from OpenAlex

Abstract In the realm of photonic integrated circuit design, on‐chip absorbers are imperative for preventing signal degradation caused by unintended stray photons, which induce signal crosstalk and operational errors. An ideal on‐chip optical absorber is expected to have a superior absorption capability across a wide range of frequencies while maintaining a minimal footprint. However, widely employed optical thin‐film absorbers suffer from limited absorption due to inherent refractive index mismatches and short light‐matter interaction lengths. Here a ≈45‐nm thin metalayer is demonstrated that exhibits uniformly high absorption over a broad wavelength range from 350 nm to 4.5 µm in a wide range of incident angles up to over 60 degrees. The metalayer's performance notably exceeds the absorption predicted by standard plane‐wave optics theories, potentially attributing to the Anderson localization effect. Integration of such a deep‐subwavelength metalayer absorber into photonic circuits will facilitate the creation of highly efficient photonic chips, propelling the advancement of optical communication and computing.

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)0.001
metaresearch head score (Gemma)0.000
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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.364
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 science0.0000.000
Research integrity0.0000.001
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 head0.012
GPT teacher head0.264
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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designSimulation or modeling
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

Citations0
Published2025
Admission routes1
Has abstractyes

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