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Record W4394572533 · doi:10.3390/en17071749

Micro Photosynthetic Power Cell Array for Energy Harvesting: Bio-Inspired Modeling, Testing and Verification

2024· article· en· W4394572533 on OpenAlexafffund
Kirankumar Kuruvinashetti, Shanmuga Sundaram Pakkiriswami, Dhilippan M. Panneerselvam, Muthukumaran Packirisamy

Bibliographic record

VenueEnergies · 2024
Typearticle
Languageen
FieldEnvironmental Science
TopicMicrobial Fuel Cells and Bioremediation
Canadian institutionsDalhousie UniversityConcordia University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsVoltagePower (physics)Maximum power principleComputer scienceElectrical engineeringBiological systemElectronic engineeringEngineeringPhysicsBiology

Abstract

fetched live from OpenAlex

A micro-photosynthetic power cell (µPSC) generates electricity through the exploitation of living photosynthetic organisms through the principles of photosynthesis and respiration. Modeling such systems will enhance insights into the µPSC that can be employed to design real-time applications from µPSC. In this study, the bio-inspired electrical equivalent modeling of the array of µPSC is elucidated. The model is validated for array configurations of the micro-photosynthetic power cells. The developed arrayed model foresees the steady-state response at various electrical loadings. The polarization characteristics of the current-voltage (I-V) and current-power (I-P) characteristics of the array of µPSC in series and parallel, and their combinations in series and parallel connected µPSCs were validated with the experimental results. From this analysis, it is predicted that the arraying of the µPSC in the combination of series and parallel is the optimal array strategy to obtain the desired voltage and current from the µPSC such that it can be used to power real-time low and ultra-low power devices.

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 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 score0.110
Threshold uncertainty score0.402

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 head0.014
GPT teacher head0.202
Teacher spread0.188 · 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.

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

Citations3
Published2024
Admission routes2
Has abstractyes

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