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Record W2947581699 · doi:10.1021/acssuschemeng.8b06501

Nanocellulose and Nanochitin Cryogels Improve the Efficiency of Dye Solar Cells

2019· article· en· W2947581699 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueACS Sustainable Chemistry & Engineering · 2019
Typearticle
Languageen
FieldEnergy
TopicTiO2 Photocatalysis and Solar Cells
Canadian institutionsUniversité de Montréal
FundersLuonnontieteiden ja Tekniikan Tutkimuksen ToimikuntaKoneen SäätiöBanting Research Foundation
KeywordsNanocelluloseElectrolyteMaterials scienceNanofiberDye-sensitized solar cellBacterial celluloseCelluloseChemical engineeringDielectric spectroscopyMembraneDegradation (telecommunications)NanotechnologyElectrochemistryChemistryElectrodeComputer science

Abstract

fetched live from OpenAlex

Biobased cryogel membranes were applied as electrolyte holders in dye solar cells (DSC) while facilitating carrier transport during operation. They also improved device performance and stability. For this purpose, cellulose nanofibers (CNF), TEMPO-oxidized CNF (TOCNF), bacterial cellulose (BC), and chitin nanofibers (ChNF) were investigated. The proposed materials and protocols for incorporating the electrolyte, via simple casting, avoided the typical problems associated with injection of the electrolyte through filling holes, a major difficulty especially in manufacturing large area cells. Owing to the fact that cryogel membranes did not require any orifice for injection, they were effective in minimizing leakage and in retaining liquid electrolyte. The results indicated the reduction of performance losses compared to conventional electrolyte filling, likely due to the better spatial distribution of electrolyte. DSCs based on BC cryogels had an initially higher performance and similar stability compared to those of the reference cells. When compared to reference cells, CNF and ChNF cryogels produced higher initial performance, but they underwent a faster degradation. The difference in stability was attributed to the effect of residual components, including lignin in CNF and proteins in ChNF, as demonstrated in bleaching experiments. TOCNF indicated a relatively poor performance, most likely because of residual aldehydes. Overall, we offer a comprehensive evaluation based on current-voltage (IV) profiles under simulated sunlight, incident photon-to-charge carrier efficiency (IPCE), electrochemical impedance spectroscopy (EIS), and color image processing, together with accelerated DSC stability tests, to unveil the effects of new membrane-based assembly. Our results give guidelines for future developments related in particular to the effects of the tested biomaterials on device stability.

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.

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 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 score0.022
Threshold uncertainty score0.863

Codex and Gemma teacher scores by category

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 communication0.0000.000
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 head0.002
GPT teacher head0.155
Teacher spread0.153 · 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