Metamaterials and Metasurfaces—Historical Context, Recent Advances, and Future Directions
Bibliographic record
Abstract
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”[1]), 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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".