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Cyanobacterial Pigments as Natural Photosensitizers for Dye-Sensitized Solar Cells

2024· preprint· en· W4401626002 on OpenAlexfundno aff
Tatiana Montagni, Mauricio Avila, Sofía Fernández, Sylvia Bonilla, Marı́a Fernanda Cerdá

Bibliographic record

VenuePreprints.org · 2024
Typepreprint
Languageen
FieldEnergy
TopicTiO2 Photocatalysis and Solar Cells
Canadian institutionsnot available
FundersPartenariat Canadien Contre Le CancerUniversidad de la República UruguayUniversity of WarwickAgencia Nacional de Investigación e InnovaciónUniversité Laval
KeywordsDye-sensitized solar cellPigmentXanthophyllCarotenoidBrown seaweedChemistryDielectric spectroscopyCyanobacteriaChemical engineeringTitanium oxideMaterials scienceAlgaePhotochemistryElectrochemistryBotanyFood scienceElectrodeOrganic chemistryBiologyBacteria

Abstract

fetched live from OpenAlex

Three filamentous freshwater cyanobacterial strains were grown at high light intensity to produce lipidic dyes composed of xanthophylls, carotenes and chlorophyll a. The properties of the pigments were evaluated as suitable natural compounds to be applied in dye-sensitized solar cells (DSSC). The assembled DSSC were characterized using the density current vs. potential profiles and electrochemical impedance spectroscopy. With an efficiency of 0.127 %, our results are higher than those previously reported using similar structured compounds from natural sources as algae and cyanobacteria, among others. The best efficiencies were probably related to the strains with high content in pigments probably related to myxoxanthophyll-like derivates and aphanizophyll, carotenoids with many hydroxyl groups able to interact with the semiconductor surface. The stability of the bonding between the dyes and the titanium oxide of the photoelectrode is crucial to ensuring acceptable performance of the DSSC, which was successfully achieved in our experiments with carotenoids with many hydroxyl groups. Our results point to cyanobacterial pigments as a promising source of natural dyes for use in solar cells.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
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 score0.048
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.004
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0020.013

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 head0.053
GPT teacher head0.314
Teacher spread0.261 · 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; both teacher heads agree on what is shown here.

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

Citations1
Published2024
Admission routes1
Has abstractyes

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