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Record W3009301521 · doi:10.1109/tap.2020.2969732

Metamaterials and Metasurfaces—Historical Context, Recent Advances, and Future Directions

2020· article· en· W3009301521 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

VenueIEEE Transactions on Antennas and Propagation · 2020
Typearticle
Languageen
FieldMaterials Science
TopicMetamaterials and Metasurfaces Applications
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsMetamaterialPermittivityNatural materialsContext (archaeology)OpticsPerspective (graphical)WavelengthPhysicsComputer scienceOptoelectronicsMaterials scienceDielectricArtificial intelligence

Abstract

fetched live from OpenAlex

The trajectory of technological progress is ultimately guided by constraints at the physical level. In building a better device or system, we are bound 1) by the properties of the materials available to us and 2) by our understanding of physical phenomena. The physical laws of the universe, immutable as they are, lead us naturally to question whether we may be able to “engineer” raw materials to better allow us to achieve, control, and manipulate natural phenomena for useful purposes. In order to do this, we must first define what we mean by the term “material.” The perception that a material must appear homogeneous to the naked eye (i.e., “a uniform goop, with no discontinuous bits and pieces” <xref ref-type="bibr" rid="ref1" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">[1]</xref> ), natural though it may be, is flawed: surely, all materials may be considered heterogeneous on some level of scale, but more importantly, this perspective is tied specifically to the electromagnetic response of these materials to wavelengths of light that are visible to the human eye. For example, although a diamond displays familiar macroscopic properties such as color, luster, and dispersion when viewed under visible light, illumination using X-rays results in a diffraction pattern that reveals its crystalline structure. Thus, the macroscopic properties of a material, e.g. polarizabilities, permittivity, permeability, refractive index, intrinsic bulk or surface impedance, and so on, are revealed only under illumination by wavelengths of light much longer than the size of its scatterers (i.e., its atoms and molecules) and their spacing (e.g., the lattice constants of a crystal). Therefore, it would seem that engineering such macroscopic properties of materials would require control of scattering at length scales of fractions—say several hundredths or even just tenths—of a wavelength, a prohibitive task if dealing in the nanometers or Angstroms. Fortunately, the reach of the electromagnetic spectrum permits us to examine the long-wavelength condition at frequencies where such length scales become much more accessible, such as the microwave and terahertz. Advances in nanoscale fabrication have extended this reach even further to infrared and visible light. At such scales, it becomes possible to synthesize scatterers to exhibit electric and magnetic responses that may then, in analogy to their natural counterparts, be homogenized to describe effective macroscopic electromagnetic properties apparent under illumination by correspondingly long wavelengths.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.602
Threshold uncertainty score0.704

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)0.0000.000

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 head0.028
GPT teacher head0.249
Teacher spread0.221 · 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