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Record W4285398697 · doi:10.1149/ma2022-01191064mtgabs

Design and Fabrication of Multiple-Color-Generating Thin-Film Optical Filters for Photovoltaic Applications

2022· article· en· W4285398697 on OpenAlexaff
Paramita Bhattacharyya, Brahim Ahammou, Fahmida Azmi, R. N. Kleiman, Peter Mascher

Bibliographic record

VenueECS Meeting Abstracts · 2022
Typearticle
Languageen
FieldEngineering
Topicsolar cell performance optimization
Canadian institutionsMcMaster University
Fundersnot available
KeywordsPhotovoltaicsPhotovoltaic systemCopper indium gallium selenide solar cellsMaterials scienceOptoelectronicsBuilding-integrated photovoltaicsFabricationOpticsEngineering physicsSolar cellElectrical engineeringEngineeringPhysics

Abstract

fetched live from OpenAlex

The use of electric vehicles (EVs) can reduce greenhouse gas emissions, air pollution, dependency on fossil fuels, and their adverse health effects on humans. But, we can only utilize the full environmental benefits of EVs when they are charged with renewable energy sources with zero or low carbon emissions. As a solution, Mobarak et al. [1] suggested integrating low-cost, flexible, and thin-film copper indium gallium selenide (CIGS) solar cells directly onto the steel of all the upward-facing body parts of the vehicles. But, this integration of solar cells comes with an aesthetic drawback. Previously, colorful photovoltaics (PVs) have been designed with one-dimensional (1D) photonic crystals or various 1D and 2D metallic nanostructures for aesthetic building-integrated photovoltaics (BIPVs) [2, 3]. However, the functionality of our application differs from that of BIPV as we need maximum absorption of the solar spectrum to obtain maximum conversion efficiency. Thus, we propose replacing the anti-reflective coating (ARC) present in the solar cells with a notch filter (a narrow high-reflection region in the visible range along with high transmission for the rest of the solar spectrum) to obtain colors. High-performance notch filters with a narrow and ultra-steep notch are well known in literature [4, 5]. Generally, high-performance notch filters are designed with a minimum of 45 layers. It is challenging to use filters with many layers on solar cells due to fabrication and thickness complexities. Thus, we created designs with a maximum of 27 layers for possible integration with photovoltaics. We used OptiLayer [6] to simulate our designs and the gradual evolution technique was used to optimize the designs. We performed our simulations with a multilayer structure of alternating high and low refractive indices of 2.09 and 1.45, respectively, on top of a silicon substrate. We optimized this multilayer structure for three reference wavelengths (400 nm, 550 nm, and 700 nm) resembling three colors. Our designs have notch widths of less than 100 nm for all the reference wavelengths with an average of 70% reflection in the high-reflection region and less than 20% reflection in the high-transmission area. To fabricate our designs, we need materials that are transparent to the solar spectrum targeted by the active material of the solar cells. The materials also need to have refractive indices closer to our simulation. Thus, we chose the combination of silicon nitride and silicon dioxide as our high and low refractive index material, respectively [7, 8]. To better understand our designs’ optical characteristics, we fabricated a scaled-down version of our structure with 5-10 layers. We used electron cyclotron resonance plasma-enhanced chemical vapor deposition (ECR-PECVD) to deposit the multilayer structure on silicon wafers. To obtain the silicon nitride and silicon dioxide layers, we used a SiH4/N2/O2/Ar precursor mixture. By tuning the gas flow rate in the reactor chamber, we tuned the stoichiometry and obtained the required refractive index for each layer. To characterize the refractive index and thickness for each layer, we used variable angle spectroscopic ellipsometry (VASE). We made a detailed comparison of our simulation and fabrication results. References [1] M. H. Mobarak, R. N. Kleiman, J. Bauman, Solar-charged electric vehicles: A comprehensive analysis of grid, driver, and environmental benefits, IEEE Transactions on Transportation Electrification 7 (2021) 579–603. doi:10.1109/TTE.2020.2996363. [2] G. Y. Yoo, et al., Multiple-color-generating cu(in,ga)(s,se)2 thin-film solar cells via dichroic film incorporation for power-generating window applications, ACS Applied Materials & Interfaces 9 (2017) 14817–14826. doi:10.1021/acsami.7b01416, pMID: 28406026. [3] K. T. Lee, et al., Colored dual-functional photovoltaic cells, Journal of Optics 18 (2016) 064003. [4] U. Schallenberg, et al., Design and manufacturing of high-performance notch filters, volume 7739, International Society for Optics and Photonics, SPIE, 2010, pp. 720 – 728. doi:10.1117/12.856580. [5] J. Zhang, et al., Design and fabrication of ultra-steep notch filters, Opt. Express 21 (2013) 21523–21529. doi:10.1364/OE.21.021523. [6] OptiLayer, 1994. URL: https://www.optilayer.com/support/faq, accessed: 2021-12-06. [7] A. Z. Subramanian, et al., Low-loss singlemode pecvd silicon nitride photonic wire waveguides for 532–900 nm wavelength window fabricated within a cmos pilot line, IEEE Photonics Journal 5 (2013) 2202809–2202809. doi:10.1109/JPHOT.2013.2292698. [8] W. D. Sacher, et al., Visible-light silicon nitride waveguide devices and implantable neurophotonic probes on thinned 200 mm silicon wafers, Opt. Express 27 (2019) 37400–37418. doi:10.1364/OE.27.037400

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.001
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)0.0010.001

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 head0.219
Teacher spread0.203 · 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
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