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Record W4404918280 · doi:10.1063/5.0234233

Multifunctional III-nitride optoelectronic system on a tiny chip

2024· article· en· W4404918280 on OpenAlexaboutno aff
Binju Wang, Jianwei Fu, Kang Fu, Ziqi Ye, Wenxuan Wu, Xumin Gao, Jiabin Yan, Yongjin Wang

Bibliographic record

VenueAIP Advances · 2024
Typearticle
Languageen
FieldEngineering
TopicSemiconductor Lasers and Optical Devices
Canadian institutionsnot available
FundersHigher Education Discipline Innovation ProjectNational Natural Science Foundation of China
KeywordsOptoelectronicsMaterials scienceNitrideChipNanotechnologyComputer scienceLayer (electronics)Telecommunications

Abstract

fetched live from OpenAlex

Multi-quantum well (MQW) diodes exhibit simultaneous emission and detection, allowing them to serve as multifunctional devices, including light emitters, receivers, energy transmitters, and information transmitters. Leveraging this capability, we designed a Multifunctional Energy Transfer Information System (METIS) that integrates contactless control, energy harvesting, and information transfer. At the core of this system, the multifunctional energy communication chip operates effectively across a broad range of extreme temperatures and in various solution environments. As the ambient temperature varies from −60 to 120 °C, the peak emission wavelength shifts from 465 to 476 nm, and even with further temperature changes from −70 to 150 °C, the communication function remains stable. Encapsulated for durability, METIS functions reliably in extreme conditions such as ice, water, salt solutions, and other light-transmitting fluids without needing external circuitry. Additionally, we demonstrate passive control of analog switches via MQW diodes. The MQW diodes also enable contactless energy and optical information transfer, ensuring stable and controllable information reconstruction at the receiving end. This approach offers an innovative solution for energy and information transmission in extreme environments.

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 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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.564
Threshold uncertainty score0.489

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 head0.007
GPT teacher head0.222
Teacher spread0.215 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2024
Admission routes1
Has abstractyes

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