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Record W3024316158 · doi:10.1149/ma2020-01452563mtgabs

Utilizing Band Diagrams to Interpret the Photovoltage and Photocurrent in Photoanodes: A Semiclassical Device Modeling Study

2020· article· en· W3024316158 on OpenAlexaff
Botong Miao, Asif Iqbal, Kirk H. Bevan

Bibliographic record

VenueECS Meeting Abstracts · 2020
Typearticle
Languageen
FieldEnergy
TopicIron oxide chemistry and applications
Canadian institutionsMcGill University
Fundersnot available
KeywordsPhotocurrentBottleneckInefficiencyComputer sciencePopulationSurface photovoltageOptoelectronicsWork (physics)VoltageEngineering physicsMaterials scienceNanotechnologyElectrical engineeringPhysicsEngineering

Abstract

fetched live from OpenAlex

Photoelectrochemcial (PEC) cells offer a promising route for supplying the world’s population with sufficient energy in an environmentally friendly manner. However, inefficiency remains a major bottleneck towards the large-scale commercialization of PEC cells. The photovoltage and photocurrent serve as important design metrics when assessing the efficiency of photoanodes within PEC cells. However, to date wide disagreement persists regarding how the photovoltage should be physically interpreted; this lack of consensus is further coupled to physical interpretations of the photocurrent. In this work, we utilize state-of-the-art device modeling to help clarify the physical origins of both the photovoltage and photocurrent in photoanodes. Through a systematic examination of a model photoanode (hematite), we correlate directly measurable current-voltage characteristics with operational band diagrams. It is shown that by directly mapping specific operating points of either equal current or equal voltage (both illuminated and in the dark) to band diagram plots, one is able to obtain substantial insights regarding the physical nature of both the photocurrent and photovoltage. By aiding the community wide effort to arrive at a consensus on these concepts, we aim to further enable the design of higher efficiency photoanodes.

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)0.001
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 score0.073
Threshold uncertainty score0.728

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.043
GPT teacher head0.287
Teacher spread0.243 · 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

Citations0
Published2020
Admission routes1
Has abstractyes

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