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Experimental Observation of Superscattering

2019· article· en· W2949381754 on OpenAlexfundno aff
Chao Qian, Xiao Lin, Yi Yang, Xiaoyan Xiong, Huaping Wang, Er‐Ping Li, Ido Kaminer, Baile Zhang, Hongsheng Chen

Bibliographic record

VenuePhysical Review Letters · 2019
Typearticle
Languageen
FieldMaterials Science
TopicMetamaterials and Metasurfaces Applications
Canadian institutionsnot available
FundersFP7 People: Marie-Curie ActionsNatural Science Foundation of Zhejiang ProvinceFundamental Research Funds for the Central UniversitiesMinistry of Education, IndiaNational Natural Science Foundation of ChinaAzrieli FoundationMinistry of Education - Singapore
KeywordsPhysicsNuclear physics

Abstract

fetched live from OpenAlex

Superscattering, induced by degenerate resonances, breaks the fundamental single-channel limit of the scattering cross section of subwavelength structures; in principle, an arbitrarily large total cross section can be achieved via superscattering. It thus provides a unique way to strengthen the light-matter interaction at the subwavelength scale, and has many potential applications in sensing, energy harvesting, bioimaging (such as magnetic resonance imaging), communication, and optoelectronics. However, the experimental demonstration of superscattering remains an open challenge due to its vulnerability to structural imperfections and intrinsic material losses. Here we report the first experimental evidence for superscattering by demonstrating the superscattering simultaneously in two different frequency regimes through both the far-field and near-field measurements. The underlying mechanism for the observed superscattering is the degenerate resonances of confined surface waves, by utilizing a subwavelength metasurface-based multilayer structure. Our work paves the way towards practical applications based on superscattering.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.040
GPT teacher head0.313
Teacher spread0.273 · 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 source (direct Gemma or distilled Codex), 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

Citations140
Published2019
Admission routes1
Has abstractyes

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