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Record W4400278943 · doi:10.1021/acsami.4c03591

Tungsten-Doped ZnO as an Electron Transport Layer for Perovskite Solar Cells: Enhancing Efficiency and Stability

2024· article· en· W4400278943 on OpenAlexaff
Munkhtuul Gantumur, Mohammad Ismail Hossain, Md. Shahiduzzaman, Asman Tamang, Junayed Hossain Rafij, M. Shahinuzzaman, Huynh Thi Cam Tu, Masahiro Nakano, Makoto Karakawa, Keisuke Ohdaira, Hamad AlMohamadi, Mohd Adib Ibrahim, Kamaruzzaman Sopian, Md. Akhtaruzzaman, Jean‐Michel Nunzi, Tetsuya Taima

Bibliographic record

VenueACS Applied Materials & Interfaces · 2024
Typearticle
Languageen
FieldEngineering
TopicPerovskite Materials and Applications
Canadian institutionsQueen's University
FundersJapan Society for the Promotion of ScienceIslamic University of MadinahKanazawa University
KeywordsMaterials sciencePerovskite (structure)DopingTungstenLayer (electronics)ElectronNanotechnologyOptoelectronicsChemical engineeringEngineering physicsMetallurgy

Abstract

fetched live from OpenAlex

This study delves into enhancing the efficiency and stability of perovskite solar cells (PSCs) by optimizing the surface morphologies and optoelectronic properties of the electron transport layer (ETL) using tungsten (W) doping in zinc oxide (ZnO). Through a unique green synthesis process and spin-coating technique, W-doped ZnO films were prepared, exhibiting improved electrical conductivity and reduced interface defects between the ETL and perovskite layers, thus facilitating efficient electron transfer at the interface. High-quality PSCs with superior ETL demonstrated a substantial 30% increase in power conversion efficiency (PCE) compared to those employing pristine ZnO ETL. These solar cells retained over 70% of their initial PCE after 4000 h of moisture exposure, surpassing reference PSCs by 50% PCE over this period. Advanced numerical multiphysics solvers, employing finite-difference time-domain (FDTD) and finite element method (FEM) techniques, were utilized to elucidate the underlying optoelectrical characteristics of the PSCs, with simulated results corroborating experimental findings. The study concludes with a thorough discussion on charge transport and recombination mechanisms, providing insights into the enhanced performance and stability achieved through W-doped ZnO ETL optimization.

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 categoriesMeta-epidemiology (narrow)
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 score0.004
Threshold uncertainty score1.000

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 head0.011
GPT teacher head0.240
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 teacher head, not a consensus.

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

Citations34
Published2024
Admission routes1
Has abstractyes

Explore more

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