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Record W7132919202

Materials Chemistry to Advance the Speed and Sensitivity of Infrared Solution-Cast Photodetectors

2023· dissertation· W7132919202 on OpenAlexaff
Maral Vafaie

Bibliographic record

VenueTSpace · 2023
Typedissertation
Language
FieldMaterials Science
TopicChemical and Physical Properties of Materials
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsPhotodetectorPassivationQuantum dotPerovskite (structure)Dark currentInfraredSemiconductorLayer (electronics)Quantum efficiency
DOInot available

Abstract

fetched live from OpenAlex

There has been considerable interest in solution-processed semiconductors for use in near-infrared (NIR) and short-wavelength infrared (SWIR) photodetectors. In this regard, perovskites and colloidal quantum dots (CQDs) are two prominent materials. I aim, in this thesis, to explore the chemistry of these material systems to enable efficient, stable, and fast solution-based infrared photodetectors. In Pb-Sn binary perovskite photodetectors, I uncover a low charge collection efficiency that results from the chemical incompatibility between tin perovskite material processing and metal oxide transport layers. I develop a new approach, revisiting the method used in chemically reducing the tin precursor. I report photodetectors with an external quantum efficiency of 85% at 850 nm, a dark current below 10-8 A/cm2, and a response time faster than 100 picoseconds. I showcase the application of these detectors in LiDAR, demonstrating sub-millimeter distance resolution. I turn my attention to SWIR InAs CQD photodiodes, finding that there is a non-optimal band alignment at the interface between the QD layer and the hole transport layer. I utilize functionalized molecular ligands to engineer the energy levels of QDs. The incorporation of molecularly tuned QDs as hole transport layers improves charge extraction efficiency from 20% to 31% at 1120 nm and leads to a two-orders-of-magnitude improvement in the dark current, reaching 4×10-7 A/cm2. I then use energy level tuning in QDs to design an electron transport layer that replaces conventional metal oxides in SWIR PbS CQD photodetectors. The favorable band alignment and improved passivation of the QD-based transport layer results in a one-order-of magnitude suppression of the dark current down to ~1×10-6 A/cm2 while maintaining an external quantum efficiency ~70% at 1450 nm. I then apply the knowledge gained on CQD surface modification to devise an efficient ligand exchange route for small-bandgap PbS CQDs. In devices, the CQD solids give rise to an external quantum efficiency greater than 80% at 1550 nm, a measured detectivity of 8×1011 Jones, and a 10 ns response time. The studies in this thesis suggest future routes to sensitive, fast, and stable photodetectors that bring out the full potential of solution-processed materials in advanced light-sensing applications.

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)0.001
metaresearch head score (Gemma)0.001
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 score0.008
Threshold uncertainty score0.025

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0080.003

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 head0.019
GPT teacher head0.290
Teacher spread0.271 · 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".

Quick stats

Citations0
Published2023
Admission routes1
Has abstractyes

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