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Record W109095409

Energy Harvesting and Modeling of Photosynthetic Power Cell

2013· dissertation· en· W109095409 on OpenAlexfundno aff
Arvind Vyas Ramanan

Bibliographic record

VenueSpectrum Research Repository (Concordia University) · 2013
Typedissertation
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicPhotosynthetic Processes and Mechanisms
Canadian institutionsnot available
FundersUniversité du Québec à Montréal
KeywordsPhotosynthesisRenewable energyPhotosystemThylakoidEnvironmental scienceBotanyChemistryBiologyPhotosystem IIChloroplastEcologyBiochemistry
DOInot available

Abstract

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The need for energy is inevitable for mankind. Climate change, depletion of natural resources, pollution and other factors have created the necessity to look for energy from renewable sources. Furthermore, there are challenges aplenty in the field of renewable energy as renewable energy sources are unpredictable, non-dependable and limited such as wind, solar photo voltaic and tidal power. Apart from these there are few unconventional renewable energy sources that have not been explored thoroughly or exploited. The photosynthetic power cell is one among them. \nThe photosynthetic power cell (PSC) harvests the energy produced at the lowest level of the food cycle which is “photosynthesis” in plants. The photosynthetic power cell extracts the energy produced during photosynthesis and respiration in form of electrical energy. The developed device differs from other published works in terms of improved performance, fabrication technique and material of structure. The two main types of sources used in the photosynthetic power cell are aerobic unicellular organisms (e.g. algae and cyanobacteria) and sub-cellular thylakoid photosystems / chloroplasts isolated from plant cells (e.g. spinach plant’s sub-cellular thylakoid photosystems isolated from the plant cells). The photosynthetic power cell produces energy under both dark and light conditions. The developed PSC is a polymer based structure instead of silicon, integrating the conventional MEMS processes with polymers. The principle of the operation of the device is based on ‘photosynthesis’. Photosynthesis and respiration both involve electron transfer chains. The electrons are extracted with the help of electrodes and a redox agent, and a power electronic converter is designed to harvest the energy. The developed device is capable of producing an open circuit voltage of 0.9 volts and about 200 μW of peak power. The μPSC has an active area of 4.84 cm2 which approximately translates to a power density of 400 mW/m2. This makes it as one of the best performing μPSC. The other top performing μPSC devices report power densities between 100 to 250 mW/m2. \nIn order to harvest energy from μPSC, power electronic converters are a necessity. Three different power electronic topologies are investigated to find the feasibility of energy harvesting using μPSC. Also, the cell should be operated at the maximum power point in order to get the best results. Common maximum power point tracking (MPPT) techniques as well as a novel MPPT technique is devised and tested for the energy harvesting application using μPSC. \nIn this thesis work, the device’s working principle, fabrication of the device and testing of the developed prototype along with the design and development of the power electronic converters with MPPT algorithm for energy harvesting application with μPSC are presented. A short introduction, basic photosynthesis process, background and history of μPSC are discussed in first chapter. The cell design, construction, working and fabrication of the cell are discussed in the second chapter. The third chapter deals with the experimental set up, characterization and testing of the cell. In the fourth chapter, modeling, analysis, simulation of PSC is executed. Analysis, identification and simulation of suitable power electronic converters with MPPT are investigated in the fifth chapter. Conclusions, future work form the epilogue.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.010
Threshold uncertainty score0.032

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0100.002

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 head0.015
GPT teacher head0.246
Teacher spread0.230 · 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 designSimulation or modeling
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
Published2013
Admission routes1
Has abstractyes

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