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Record W4407224784 · doi:10.1021/acsphotonics.4c02258

Breaking the Size Limit of Room-Temperature Prepared Lead Sulfide Colloidal Quantum Dots for High-Performance Short-Wave Infrared Optoelectronics

2025· article· en· W4407224784 on OpenAlexaff
Yin-Fen Ma, Jian Xu, Ke-Lei Zu, Youmei Wang, Juntao Hu, Nan Chen, Dongming Zhang, Ao Li, Dengke Wang, Huaiyi Ding, Mei Leng, Yong‐Biao Zhao, Zheng‐Hong Lu

Bibliographic record

VenueACS Photonics · 2025
Typearticle
Languageen
FieldMaterials Science
TopicQuantum Dots Synthesis And Properties
Canadian institutionsUniversity of Toronto
FundersMajor Science and Technology Projects in Yunnan ProvinceNational Natural Science Foundation of China
KeywordsLead sulfideQuantum dotMaterials scienceInfraredNanotechnologyColloidOptoelectronicsLimit (mathematics)SulfideOpticsChemistryPhysicsMetallurgyPhysical chemistry

Abstract

fetched live from OpenAlex

Lead sulfide (PbS) colloidal quantum dots (CQDs) are of great interest for short-wave infrared (SWIR) optoelectronic devices due to their tunable bandgaps across the whole SWIR spectra. PbS CQD inks synthesized directly at room temperature (RT) and ready for the fabrication of various SWIR devices are highly demanded. There are currently no available protocols for RT synthesis of PbS CQDs with absorption beyond 1200 nm. Here, we report on the first synthesis of PbS CQDs at RT with an absorption beyond 1800 nm. There is a delicate balance between nucleation of new seeds and growth of existing dots regulated by the lead-to-sulfur (Pb/S) precursor ratio in the reaction medium, and a proper Pb/S ratio ranging from 1.1 to 2 should be maintained to keep the continuous growth. Photodiodes based on PbS CQDs with a 1550 nm excitonic absorption are fabricated to demonstrate their suitability for device applications. The resulting devices achieve a high photo responsivity of 0.635 A/W, a specific detectivity of 1.01 × 10 11 Jones, and a fast response with rise and fall times of 1.08 and 1.10 μs, respectively.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.011
Threshold uncertainty score0.961

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.016
GPT teacher head0.242
Teacher spread0.225 · 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 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".

Quick stats

Citations11
Published2025
Admission routes1
Has abstractyes

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