Bibliographic record
Abstract
Tellurium-based compounds such as cadmium telluride (CdTe) and mercury cadmium telluride (HgCdTe) have been used as infrared (IR) detectors for over half a century. These versatile narrow gap semiconducting materials are characterized by a direct energy gap and have the ability to obtain both high and low carrier concentrations, high electron mobility of electrons, and low dielectric constant. Nanophotosensors with cadmium chalcogenide (Te, Se, and S) semiconductor nanocrystals are considered to be best candidates to detect spacecraft cracks without increasing payload or changing the thermal properties of heat-shielding of spacecraft. Hg1−x Cd x Te (MCT) is the most widely used infrared (IR) detector material in military applications, compared to other IR detector materials, primarily because of two key features: it is a direct energy band gap semiconductor and its band gap can be engineered by varying the Cd composition to cover a broad range of wavelengths. A small change of lattice constant with composition makes it possible to grow high-quality layers and heterostructures. These can thus be used for detectors operated at various modes, and can be optimized for operation spanning the wide range of the IR spectrum (short-wave infrared (SWIR): 1–3 μm, middle wavelength IR (MWIR: 3–5 μm; long-wavelength IR: 8–14 μm) to very long-wave infrared (VLWIR): 14–30 μm, and at temperatures ranging from that of liquid helium to room temperature. Other specific advantages include a direct energy gap, ability to obtain both low and high carrier concentrations, high mobility of electrons, and low dielectric constant. However, in spite of the various advantages, the material suffers from technological disadvantages partly due to the presence of a weak Hg–Cd bond, which results in bulk, surface, and interface instabilities. Uniformity and yield are still issues especially in the long-wavelength infrared (LWIR) region. Nevertheless, these are leading candidates for IR photoconductive and photovoltaic detector materials in particular for military and space applications. This chapter reviews the development and applications of these materials and competitive technologies for IR detection.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.088 | 0.080 |
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".