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Record W2741691086

Infrared Sensitive Solution-processed Quantum Dot Photovoltaics in a Nanoporous Architecture

2009· dissertation· en· W2741691086 on OpenAlexfundno aff
Ethan J. D. Klem

Bibliographic record

VenueTSpace · 2009
Typedissertation
Languageen
FieldMaterials Science
TopicQuantum Dots Synthesis And Properties
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of CanadaUniversity of Toronto
KeywordsPhotovoltaicsQuantum dotNanoporousArchitectureNanotechnologyOptoelectronicsMaterials sciencePhotovoltaic systemEngineeringElectrical engineeringGeography
DOInot available

Abstract

fetched live from OpenAlex

If solar energy is to be a significant component of our energy supply, technologies are required which produce high efficiency solar cells using inexpensive materials and versatile manufacturing processes. Solution-processed materials have been used to create low cost, easily fabricated devices, but have suffered from low power conversion efficiencies. A lack of infrared energy capture limits their efficiency. In this work we develop solution-processed photovoltaic devices using lead sulphide quantum dots and high surface area porous oxide electrodes. The resultant devices have a spectral response from 400 to 1800 nm. In fabricating these devices we utilize crosslinking molecules. We explore the impact crosslinkers have on the mobility and morphology of quantum dot films using field effect transistors and transmission electron microscopy. We also explore a hybrid organic/inorganic route for controlling the net doping in quantum dot films. We investigate the chemical and compositional changes that lead sulphide quantum dots films undergo during crosslinker treatment and annealing. Using this information we optimize our charge separation efficiency and our open circuit voltage. The resulting devices have an infrared power conversion efficiency of 2%, four orders of magnitude higher than that in previously reported lead sulphide quantum dot devices.

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 categoriesMeta-epidemiology (narrow)
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 score0.010
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
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 head0.017
GPT teacher head0.283
Teacher spread0.266 · 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.

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
Published2009
Admission routes1
Has abstractyes

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