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Record W4285399345 · doi:10.1149/ma2022-011144mtgabs

On the Underlying Electronic Transfer of a Photo-Electrode for Developing a Photo-Battery

2022· article· en· W4285399345 on OpenAlexaff
Elsa Briqueleur, Mickaël Dollé, Will Skene

Bibliographic record

VenueECS Meeting Abstracts · 2022
Typearticle
Languageen
FieldMaterials Science
TopicConducting polymers and applications
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsBattery (electricity)ElectrodeTransfer (computing)Computer scienceOptoelectronicsMaterials scienceElectrical engineeringPhysicsEngineeringQuantum mechanics

Abstract

fetched live from OpenAlex

Addressing the current global energy consumption needs and reducing the carbon footprint of primary non-renewable energy resources are key challenges of the 21st century. Solar energy is among one of the best suited candidates to address this along with the current global energy needs. This is because sunlight produces no greenhouse emissions. A major challenge that must be overcome before the benefits of solar energy can be truly realized is the storage of sunlight for use during dark periods. Devices that convert, store, and deliver the stored solar energy during dark periods are therefore of importance. Lithium-ion batteries are proven technologies. They have a high energy density along with a long cycle life. Indeed, they have been successfully coupled to silica-based solar cells such as photovoltaic panels. To expand upon the proven solar-battery technologies and move towards self-sustaining and portable energy devices, our current efforts focus on merging a lithium-ion battery with a photoactive organic dye. The working principle of this all-in-one device is the energy harvesting of sun light by an organic dye and storing the harvesting energy as chemical energy, courtesy of redox reactions that are specific to the lithium-ion battery. The organic dye of choice was selected because it satisfies many of the physical and electrochemical requirements for its use in an integrated photobattery. For example, it possesses an intrinsic broad absorption in the visible spectrum, has a high degree of colorfast, and is photostable. Despite these key properties that are ideal for its use in a photobattery, this dye has not been used as the photoactive layer in a photobattery. In addition, many LIB electrode materials that meet the energetic requirements to be paired with the dye need to be screened. Towards demonstrating the compatibility of the dye-battery active material, the photoreduction of the dye by the battery’s electroactive component will be demonstrated in solution by fluorescence spectroscopy. Both steady-state and time-resolved quenching measurements will be presented as sound evidence of the underpinning electron transfer between the constitutional “solar” and “battery” components. Various common electrode materials have also been screened to provide key knowledge about the materials’ property requirements to sustain intermolecular electronic transfer. Different architectures of photo-electrodes will also be explained and evaluated for an optimal electronic transfer. Steady-state quenching measurements and Raman spectroscopy will be shown to broaden the understanding of the interfacial electron transfer processes. Electrochemical studies will also be presented to complement the photophysical investigation, including galvanostatic cycling with the dye using various photo-cathode architectures. These were investigated to understand the role of the microstructure on the electronic transfer. The collective studies will provide sound evidence that both the photoactive and batteries technologies can be successfully merged, laying the experimental groundwork for an all-in-one photobattery. Towards realizing the true ecological potential of the battery, it will be presented that its conventional organic electrolytic solvents can be replaced with environmentally benign water.

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)0.000
metaresearch head score (Gemma)0.000
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: none
Teacher disagreement score0.007
Threshold uncertainty score0.024

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0070.005

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 head0.049
GPT teacher head0.283
Teacher spread0.234 · 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
Published2022
Admission routes1
Has abstractyes

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