Breaking the Built‐In Electric Field Barrier in p–n Heterojunction for Self‐Powered, Wavelength Distinguishable Photoelectrochemical Photodetectors: Toward Low Power Consumption and Secure Underwater Wireless Sensor Network
Bibliographic record
Abstract
Abstract Self‐powered, light wavelength distinguishable photodetectors (PDs) are appealing components to build a robust, secure, and low energy consumption underwater wireless sensor network (UWSN). However, achieving such devices is extremely difficult even today. In this context, the first self‐powered, light wavelength distinguishable PDs with photoelectrochemical (PEC) principles and using tunnel junction (TJ) to overcome the technical hurdles for self‐powered, light wavelength distinguishable PEC‐PDs with p–n junction working electrode is reported. For such devices, a single photoelectrode is used, that is, one photoelectrode is able to distinguish different light wavelengths without using any external electrical power, and they are able to distinguish light wavelengths in both the ultraviolet (UV) and blue wavelength ranges. High responsivities reaching mA/W range and ultrafast response time with less than 10 ms are achieved in self‐powered operation mode. Moreover, such devices are able to operate not only in acidic but also in NaCl electrolyte, making them potentially attractive for applications in ocean environment. In the end, it is demonstrated that leveraging such PEC‐PDs, excellent data security can be achieved in the data communication mimicking that in an UWSN in ocean environment. This study not only represents a breakthrough in PDs, but also significantly advances the development of UWSNs, especially for ocean environment.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".