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Record W4285399741 · doi:10.1149/ma2022-017646mtgabs

(Digital Presentation) A Highly Efficient Photosupercapacitor By Integration of a Mesoporous N-Doped Carbon Double Layer Capacitor with a Perovskite Solar Cell

2022· article· en· W4285399741 on OpenAlexaff
Taisiia Berestok, Christian Diestel, Niklas Ortlieb, Stefan W. Glunz, Anna Fischer

Bibliographic record

VenueECS Meeting Abstracts · 2022
Typearticle
Languageen
FieldMaterials Science
TopicSupercapacitor Materials and Fabrication
Canadian institutionsInnovation Cluster (Canada)
Fundersnot available
KeywordsSupercapacitorMaterials scienceEnergy storageSolar cellAuxiliary electrodePhotovoltaic systemNanotechnologyOptoelectronicsPerovskite solar cellElectrodeElectrical engineeringCapacitancePower (physics)EngineeringChemistryElectrolyte

Abstract

fetched live from OpenAlex

Internet of Things devices – wireless sensors and actuators – require long-term off-grid power sources that would be cheap, and at the same time have a small footprint with low environmental impact throughout their life cycle. Such an off-grid power source could be realized with a photosupercapacitor: A solar cell is integrated with an electrochemical double-layer capacitor (EDLC) into a single monolithic device using a three-electrode design, where the two components share a common electrode.1 The solar cell converts ambient light into an electric current and charges the integrated supercapacitor via the common electrode, which in turn powers the external device. The supercapacitor, on the other hand, acts as a buffer, mediating between an intermittent light source and the load of the device. In terms of footprint and hence energy and power density, integrated monolithic photosupercapacitors have a clear advantage in comparison with simply wiring a solar cell to a supercapacitor as storage unit. When compared to batteries or pseudocapacitors that involve slow solid-state diffusion reactions and/or surface redox processes, EDLCs are particularly suited for the integration with solar cells into photosupercapacitors as they are able to operate efficiently in fast fluctuating environments where rapid on/off cycles are required without the need to supply a fixed voltage onset.2 Owing to their large specific surface area, good electrical conductivity, and electrochemical stability, mesoporous nitrogen-doped carbon nanospheres (MPNCs) show a great promise for being implemented as an electrode material for supercapacitors.3 , 4 To synthesize MPNCs we used a hard-templating strategy based on the polymerization and self-assembly of aniline in the presence of SiO2 nanoparticles (7 nm), which led to the formation of spherical SiO2/Polyaniline composites. Their carbonization and template removal resulted in highly monodisperse, 140 nm-diameter spherical MPNCs with a large specific surface area (825 m2 g-1) and defined mesopores (7 nm). The MPNCs are easily dispersible and could be processed by doctor-blading in homogeneous 3D-percolated electrodes. Benefiting from the well-defined mesoporous network and the highly accessible electrochemical surface area, our MPNC-based gel-electrolyte freestanding EDLC delivered large energy and power densities and a high capacitance (400 F g-1 at 1 A g-1) with high (95 %) coulombic efficiency. To achieve an energy-autonomous self-powered system, we combined the MPNC-based EDLCs with halide-perovskite-based solar cells in a monolithic three-electrode fashion. As the solar cell we used a p-i-n perovskite solar cell with a FA0.75Cs0.25Pb(I0.8Br0.2)3 (high bandgap) absorber and large photosensitive area (1 cm2) delivering a high open-short circuit voltage (VOC ) of 1.08 V and a short-circuit current (JSC ) of 17.9 mA/cm2. To facilitate the assembly process and to address the adverse sensitivity of the perovskite layer to the electrolyte solvent, we optimized the solar cell layer sequence and termination. At the same time, we used a semi-solid gel electrolyte for the EDLC, which minimizes the contact of the perovskite layer with the electrolyte solvent. This allowed us to obtain a free-standing integrated photosupercapacitor without the necessity of any encapsulation. This reduces the overall device footprint and cost, and simplifies the assembly. Benefiting from the high efficiency of the solar cell and the excellent performance of the EDLC, the integrated hybrid photosupercapacitor demonstrated fast (< 5 s) photocharging up to 1 V under 1 sun illumination and discharge through the EDLC terminals, proving that the solar energy was harvested, converted, and stored. The photosupercapacitor delivered 4.27 μWh/cm2 at a power density of 0.29 mW/cm2 and 1.68 μWh/cm2 at 2.2mW/cm2 with an areal capacitance of 31 mF/cm2. This resulted in an unprecedented overall electrochemical energy conversion efficiency of 11.5 %.5 The strategy for photosupercapacitor fabrication presented here was extended to the integration of MPNC-based EDLCs with other types of solar cells, depending on the intended application. The achieved results pave the way towards the development of off-grid powered energy-autonomous devices. 1 Q. Zeng, Y. Lai, L. Jiang, F. Liu, X. Hao, L. Wang and M. A. Green, Adv. Energy Mater., 2020, 10, 1–30. 2 F. Béguin, V. Presser, A. Balducci and E. Frackowiak, Adv. Mater., 2014, 26, 2219–2251. 3 J. Melke, R. Schuster, S. Möbus, T. Jurzinsky, P. Elsässer, A. Heilemann and A. Fischer, Carbon N. Y., 2019, 146, 44–59. 4 J. Melke, J. Martin, M. Bruns, P. Hu, A. Scho, A. Fischer, S. M. Isaza, F. Fink and P. Elsa, ACS Appl. Energy Mater, 2020, 3, 11627. 5 T. Berestok, C. Diestel, N. Ortlieb, J. Buettner, J. Matthews, P. S. C. Schulze, J. C. Goldschmidt, S. W. Glunz and A. Fischer, Sol. RRL, 2021, 5, 1–13.

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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.107
Threshold uncertainty score0.357

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.1070.027

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 head0.013
GPT teacher head0.219
Teacher spread0.206 · 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".

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Citations0
Published2022
Admission routes1
Has abstractyes

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