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Record W4386265365 · doi:10.1039/d3nr03005a

MXene-modified electrodes and electrolytes in dye-sensitized solar cells

2023· review· en· W4386265365 on OpenAlexaff
Sikandar Aftab, Muhammad Zahir Iqbal, Sajjad Hussain, Fahmid Kabir, H.H. Hegazy, Burragoni Sravanthi Goud, M. Aslam, Fan Xu

Bibliographic record

VenueNanoscale · 2023
Typereview
Languageen
FieldEnergy
TopicAdvanced Photocatalysis Techniques
Canadian institutionsSimon Fraser University
FundersKing Khalid University
KeywordsDye-sensitized solar cellElectrolyteMaterials scienceElectrodeRenewable energyEnergy conversion efficiencyEnergy transformationNanotechnologyOptoelectronicsChemistryElectrical engineeringEngineering

Abstract

fetched live from OpenAlex

, and hybrids like 2D-2D heterostructures for electrode modification in photovoltaics (PVs), including anodes, photoanodes, composite decorated electrodes, counter electrodes (CEs), and electrolytes, is provided in this review article. The effects on the performance metrics of various deposition techniques are discussed and assessed. The use of MXene-modified electrodes in DSSCs suggests potential for enhancing the performance and efficiency of these solar cells in general. The article examines this strategy's potential advantages and implications, illuminating the fascinating advancements in the area and emphasizing MXenes' potential as a valuable substance for renewable energy applications. We also discuss the difficulties and potential benefits of using MXene-modified electrodes in DSSCs and emphasize the need for additional study to enhance stability, optimize MXene integration techniques, and enhance long-term device performance. The scalability and potential of MXene-based electrode modifications for commercial applications are also covered, addressing issues and prospects for the future, focusing on the necessity of more study. Electrodes modified with MXenes can improve DSSC performance and advance sustainable energy conversion.

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: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.974
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
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 head0.030
GPT teacher head0.321
Teacher spread0.291 · 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 designOther design
Domainnot available
GenreReview

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

Citations24
Published2023
Admission routes1
Has abstractyes

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