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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 (Cs<sub>2</sub>AgBiBr<sub>6</sub>) 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 TiO<sub>2</sub> and Cs<sub>2</sub>AgBiBr<sub>6</sub> can effectively promote the transport and extraction of carriers in the device. However, there are few reports on the use of TiO<sub>2</sub> nanopillar arrays as a transport layer to improve the performance of Cs<sub>2</sub>AgBiBr<sub>6</sub> photodetectors. In this work, high-quality Cs<sub>2</sub>AgBiBr<sub>6</sub> 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 TiO<sub>2</sub> nanopillar arrays are embedded into the Cs<sub>2</sub>AgBiBr<sub>6</sub> 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 Cs<sub>2</sub>AgBiBr<sub>6</sub> double perovskite photodetectors based on TiO<sub>2</sub> 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/cm<sup>2</sup>, the responsivity is 0.019 A/W and 0.057 A/W, respectively, and the specific detectivity is 1.9 × 10<sup>10</sup> Jones and 5.6 × 10<sup>10</sup> Jones, respectively. Comparing with the Cs<sub>2</sub>AgBiBr<sub>6</sub> perovskite photodetector based on a planar TiO<sub>2</sub> 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 Cs<sub>2</sub>AgBiBr<sub>6</sub> photodetector is improved by using TiO<sub>2</sub> nanopillars as an electron transport layer. It provides a reference solution for developing high-performance Cs<sub>2</sub>AgBiBr<sub>6</sub> 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 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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Scholarly communication, Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesMeta-epidemiology (narrow)
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 score0.283
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0040.004
Meta-epidemiology (broad)0.0040.002
Bibliometrics0.0020.004
Science and technology studies0.0020.001
Scholarly communication0.0020.002
Open science0.0030.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0010.004

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; both teacher heads agree on what is shown here.

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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