Fundamental Characteristics of Photodetectors and Applications of Two-Dimensional Materials in Photodetection
Bibliographic record
Abstract
Two-dimensional (2D) nanomaterials, due to their atomic-level thickness, tunable bandgap, and strong light-matter interaction, have emerged as a transformative platform for high-performance photodetectors. In recent years, devices based on emerging materials such as graphene, transition metal dichalcogenides (TMDs), Bi2O2Se, and InSe have achieved record-breaking light response, detection rate, and ultrafast response times. This article reviews performance optimization strategies, including heterostructure design, defect and doping control, interface passivation, and novel device structures (such as self-powered and flexible devices). It systematically compares key performance indicators such as response rate, external quantum efficiency, specific detection rate, dark current, and time-domain response. It assesses the trade-off between gain and speed, as well as challenges in large-scale fabrication, device consistency, and multi-functional integration. Finally, it looks forward to new directions such as wafer-level direct growth, polarization-sensitive detection, plasma or cavity enhancement technology to promote the development of next-generation wide-spectrum, low-power, and flexible photodetectors.
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 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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".