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Record W4389541339 · doi:10.17118/11143/20830

Investigation of Zro2/Sio2 photonic crystal optical filter with gradedrefraction indices in thermophotovoltaic systems

2023· article· en· W4389541339 on OpenAlexaff
Mehran Sepah Mansoor, Nima Talebzadeh, Paul G. O’Brien

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicPhotonic and Optical Devices
Canadian institutionsYork University
Fundersnot available
KeywordsThermophotovoltaicPhotonic crystalOptical filterMaterials scienceOptoelectronicsOpticsRefractive indexRefractionPhotonicsOptical materialsFilter (signal processing)Computer sciencePhysicsCommon emitter

Abstract

fetched live from OpenAlex

Thermophotovoltaics (TPV) systems convert radiant heat into electric power. TPV has many applications including uninterruptable power supplies, self-powered heating devices and power generation in space. Recently, the important role of TPV in transitioning to clean energy has been recognized, as research has increasingly been directed toward the application of TPV systems for industrial waste heat recovery, solar thermophotovoltaics, and thermal energy grid storage. The main components in a TPV system are the photovoltaic (PV) cell and the emitter. The emitter is at a high temperature in the range of ~1000 K to 2500 K. Radiation from the emitter is incident onto a PV cell with a low band-gap. Incident photons that have an energy that is greater than the band-gap of the PV cell (referred to as in-band photons) can be converted to electric power. Incident photons with energy less than the band-gap of the PV cell (referred to as out-of-band photons) do not contribute to the electric power output. In a TPV system a filter that transmits in-band photons while reflecting out-of-band photons can be placed between the emitter and the PV cell to enhance its performance. The reflected out-of-band photons can be absorbed by the emitter such that their energy is "recycled". However, a challenge with filters presently used in TPV system is simultaneously achieving very high transmittance and reflectance over a broad spectral range for in-band and out-of-band filters, respectively. Herein the design and optimization of a ZrO2/SiO2 one-dimensional photonic crystal (1D-PC) optical filter for TPV systems is investigated. A novel feature of the 1D-PC ZrO2/SiO2 filters investigated in this work is that they have a graded index of refraction profile at the interfaces between the layers within the filter, which increases their transmittance of in-band photons. COMSOL Multiphysics software and MATLAB are used to evaluate the transmittance and reflectance of the filters for different design configurations. Also, MATLAB is used to calculate the efficiency and output power density of the filters within a TPV system comprising a blackbody emitter and a GaSb PV cell. The results show that by stacking two ZrO2/SiO2 1D-PC filters with peak wavelength positions of 2050 nm and 3450 nm a TPV system efficiency of 26% and a power density of 8.3 W/cm 2 are achieved. These results show the ZrO2/SiO2 1D-PC filters studied in this work can be used in TPV systems to reduce their spectral losses and boost their performance.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.017
GPT teacher head0.210
Teacher spread0.193 · 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

Citations0
Published2023
Admission routes1
Has abstractyes

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