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Record W4390698272 · doi:10.7498/aps.73.20231919

Multi-band response Cs<sub>2</sub>AgBiBr<sub>6</sub> double perovskite photodetector based on TiO<sub>2</sub> nanopillars

2024· article· en· W4390698272 on OpenAlexfundno aff
Tangyou Sun, Yanli Yu, Zu-Bin Qin, Z. Z. Chen, Junli Chen, Yue Jiang

Bibliographic record

VenueActa Physica Sinica · 2024
Typearticle
Languageen
FieldEngineering
TopicPerovskite Materials and Applications
Canadian institutionsnot available
FundersGuangxi Provincial Key Laboratory of Precision Navigation Technology and Application, Guilin University of TechnologyScience and Technology Department of Guangxi Zhuang AutonomousNational Natural Science Foundation of ChinaBritish Columbia Innovation Council
KeywordsPhotodetectorMaterials scienceOptoelectronicsPerovskite (structure)PhysicsChemistryCrystallography

Abstract

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Photodetectors are widely used in the fields of environmental monitoring, medical analysis, security surveillance, optical communication and biosensing due to their high responsiveness, fast response time, low power consumption, good stability and low processing cost. Fully inorganic lead-free perovskite material (Cs2AgBiBr6) has received a lot of attention in recent years in the research of photodetector applications due to its advantages of long carrier lifetime, high stability, moderate forbidden bandwidth, and environmental friendliness. For perovskite photodetectors, the semiconductor nanopillar array structure can effectively reduce the reflection loss of light from the surface to improve the absorption of incident light in the device and inhibit the exciton complexes in the device, and the good energy level matching between TiO2 and Cs2AgBiBr6 can effectively promote the transport and extraction of carriers in the device. However, there are few reports on the use of TiO2 nanopillar arrays as a transport layer to improve the performance of Cs2AgBiBr6 photodetectors. In this work, high-quality Cs2AgBiBr6 thin films with large grain size, no visible pinholes, and good uniform coverage are successfully prepared by a low-pressure-assisted spin-coating method under ambient conditions. Hydrothermally grown TiO2 nanopillar arrays are embedded into the Cs2AgBiBr6 layer to form a close core-shell structure, increasing the physical contact area between the two to ensure more effective electron injection and charge separation, and to improve the carrier transport efficiency in the device. Multi-band responsive Cs2AgBiBr6 double perovskite photodetectors based on TiO2 nanopillars are excited at multiple wavelengths of 365 nm and 405 nm with high light response and good stability and reproducibility, resulting in average switching ratios of 522 and 2090, respectively. When the light source is excited at 365 nm and 405 nm with a light intensity of 0.056 W/cm2, the responsivity is 0.019 A/W and 0.057 A/W, respectively, and the specific detectivity is 1.9 × 1010 Jones and 5.6 × 1010 Jones, respectively. Comparing with the Cs2AgBiBr6 perovskite photodetector based on a planar TiO2 electron transport layer, the average switching ratios are improved by a factor of 65 and 110, the responsivities are improved by 35% and 256%, and the specific detectivity are improved by a factor of 6.9 and 25, respectively. In this work, the photoelectric performance of Cs2AgBiBr6 photodetector is improved by using TiO2 nanopillars as an electron transport layer. It provides a reference solution for developing high-performance Cs2AgBiBr6 perovskite photodetectors in future.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

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 communication0.0000.001
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.001

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.

Opus teacher head0.015
GPT teacher head0.241
Teacher spread0.227 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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".

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Citations0
Published2024
Admission routes1
Has abstractyes

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