Surface chemistry-engineered perovskite quantum dot photovoltaics

Chemical Society Reviews, Год журнала: 2025, Номер unknown

Опубликована: Янв. 1, 2025

This review summarizes the progress and provides perspectives on perovskite quantum dot photovoltaics, with a focus surface chemistry engineering, paving new direction for large-area low-cost PV technology to address major energy challenges.

Язык: Английский

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Cyanovinyl Phosphonic Acid Based Molecular Additives for Highly Efficient and Stable Formamidinium‐Cesium Lead Lodide Perovskite Solar Cells

Small, Год журнала: 2025, Номер unknown

Опубликована: Март 19, 2025

Abstract Formamidinium‐cesium lead iodide perovskites (FA 1‐x Cs x PbI 3 , 0 < 0.1) are promising solar cell absorber materials with favorable bandgap and high thermal stability. However, the fabrication of high‐quality FA films large grain size, stable black phase, uniform cations distribution, minimal defects remains challenging. Here, efficacy cyanovinyl phosphonic acid (CPA) based molecular additives in fabricating 0.95 0.05 is reported. The CPA unit shows strong interactions all species (PbI 2 ), formamidinium (FAI), cesium (CsI) precursor solution, thus significantly alleviating inhomogeneous crystallization this mixed‐cation system. resulting exhibit enlarged size homogenized cation presence CPA‐based molecules final perovskite enhances optoelectronic qualities photostability owing to efficient passivation interaction perovskite. With optimizations on adding concentrations, inverted structured cells an optimal additive (Ph‐CPA) achieve power conversion efficiencies (PCEs) up 26.25%. Moreover, lifespans (T90, time corresponding 90% initial PCE retained) devices unprecedentedly prolonged from hundreds hours over 1000 3000 h under light stresses (ISOS‐L‐2I, 85 °C) operational condition (ISOS‐L‐1I), respectively.

Язык: Английский

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The state of the art in photovoltaic materials and device research

Nature Reviews Materials, Год журнала: 2025, Номер unknown

Опубликована: Март 20, 2025

Язык: Английский

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Accelerated discovery of high-performance small-molecule hole transport materials via molecular splicing, high-throughput screening, and machine learning

Journal of Materials Informatics, Год журнала: 2025, Номер 5(3)

Опубликована: Апрель 15, 2025

As the most representative and widely utilized hole transport material (HTM), spiro-OMeTAD encounters challenges including limited mobility, high production costs, demanding synthesis conditions. These issues have a notable impact on overall performance of perovskite solar cells (PSCs) based hinder its large-scale commercial application. Consequently, there exists strong demand for high-throughput computational design novel small-molecule HTMs (SM-HTMs) that are cost-effective, easy to synthesize, offer excellent performance. In this study, systematic iterative development process SM-HTMs is proposed, aiming accelerate discovery application high-performance SM-HTMs. A custom-developed molecular splicing algorithm (MSA) generated sample space 200,000 intermediate molecules, culminating in creation comprehensive database over 7,000 potential SM-HTM candidates. total, six promising HTM candidates were identified through MSA, density functional theory calculations screening. Furthermore, three machine learning algorithms, namely random forest, gradient boosting decision tree, extreme (XGBoost), employed construct predictive models key properties, recombination energy, solvation free maximum absorption wavelength, hydrophobicity. Among these, XGBoost-based model demonstrated best The MSA methodology combining prediction models, as introduced offers powerful universal toolkit optimization next-generation SM-HTMs, thereby paving way future advancements PSCs.

Язык: Английский

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Advancing perovskite photovoltaic technology through machine learning‐driven automation

InfoMat, Год журнала: 2025, Номер unknown

Опубликована: Фев. 24, 2025

Abstract Since its emergence in 2009, perovskite photovoltaic technology has achieved remarkable progress, with efficiencies soaring from 3.8% to over 26%. Despite these advancements, challenges such as long‐term material and device stability remain. Addressing requires reproducible, user‐independent laboratory processes intelligent experimental preselection. Traditional trial‐and‐error methods manual analysis are inefficient urgently need advanced strategies. Automated acceleration platforms have transformed this field by improving efficiency, minimizing errors, ensuring consistency. This review summarizes recent developments machine learning‐driven automation for photovoltaics, a focus on application new transport discovery, composition screening, preparation optimization. Furthermore, the introduces concept of self‐driven Autonomous Material Device Acceleration Platforms (AMADAP) discusses potential it may face. approach streamlines entire process, discovery performance improvement, ultimately accelerating development emerging technologies. image

Язык: Английский

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An Automated Workflow to Discover the Structure–Stability Relations for Radiation Hard Molecular Semiconductors

Journal of the American Chemical Society, Год журнала: 2025, Номер unknown

Опубликована: Янв. 3, 2025

Emerging photovoltaics for outer space applications are one of the many examples where radiation hard molecular semiconductors essential. However, due to a lack general design principles, their resilience against extra-terrestrial high-energy can currently not be predicted. In this work, discovery materials is accelerated by combining strengths high-throughput, lab automation and machine learning. This way, large material library more than 130 organic hole transport automatically processed, degraded, measured. The degraded under ultraviolet-C (UVC) light in nitrogen atmosphere, serving as conditions electromagnetic hardness tests. A value closely related differential quantum yield photodegradation extracted from evolution UV–visible (UV–vis) spectra over time used stability target. Following procedure, ranking spanning 3 orders magnitude was obtained. Combining Gaussian Process Regression based on predictors structural fingerprints manual filtering features, structure–stability relations UVC stable could found: Fused aromatic ring clusters beneficial, whereas thiophene, methoxy vinylene groups detrimental. Comparing UV–vis film solution, bond cleavage made out leading degradation mechanism. Even though principle break most bonds, able distribute dissipate energy well enough so that chemical structures remain stable. established predictive model quantifies effect specific features stability, allowing chemists consider strategy. future, larger data set will allow inversely which show high performance at same time.

Язык: Английский

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Thermodynamically‐Driven Phase Engineering and Reconstruction Deduction of Medium‐Entropy Prussian Blue Analogue Nanocrystals

Advanced Materials, Год журнала: 2025, Номер unknown

Опубликована: Апрель 14, 2025

Abstract Prussian blue analogs (PBAs) are exemplary precursors for the synthesis of a diverse array derivatives.Yet, intricate mechanisms underlying phase transitions in these multifaceted frameworks remain formidable challenge. In this study, machine learning‐guided analysis medium‐entropy PBA system is delineated, utilizing an descriptors that encompass crystallographic phases, structural subtleties, and fluctuations multimetal valence states. By integrating multimodal simulations with experimental validation, thermodynamics‐driven transformation model established accurately predicted critical parameters. A constellation advanced techniques—including atomic force microscopy coupled Kelvin probe individual nanoparticles, X‐ray absorption spectroscopy, operando ultraviolet‐visible situ diffraction, theoretical calculations, multiphysics simulations—substantiated iron oxide@NiCoZnFe‐PBA exhibits both exceptional stability remarkable electrochemical activity. This investigation provides profound insights into transition dynamics polymetallic complexes propels rational design other thermally‐induced derivatives.

Язык: Английский

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Self-Assembled Monolayers for Improved Performance In Flexible P-I-N Perovskite Solar Cells

Опубликована: Янв. 1, 2025

Flexible perovskite solar cells (F-PSCs) have attracted enormous research interest in wearable and portable electronics because of their lightweight, high flexibility portability. However, power conversion efficiency (PCE) stability still lag far behind rigid devices the soft inhomogeneous nature flexible substrate utilized F-PSCs. Herein, we introduce MeO-2PACz as self-assembled monolayers (SAMs) to modify perovskite/HTL (hole transport layer) interface F-PSCs high-quality thin films are grown. In addition, owing coordination reaction between phosphonic acid group Pb2+ SAMs, defects lattice passivated effectively, trap states probability trap-assisted nonradiative recombination reduced. Finally, impressive PCE 15.34% is achieved with superior SAM-modified device, compared control device a 13.27%. Through this work, provide valuable insights references for further investigation utilization SAMs inverted F- PSCs applications.

Язык: Английский

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High-Throughput Machine Learning and Experimental Validation Unveils Giant Responsivity for Extreme Ultraviolet Detectors

Research Square (Research Square), Год журнала: 2025, Номер unknown

Опубликована: Янв. 28, 2025

Identifying materials with optimal optoelectronic properties for targeted applications represents both a critical need and persistent challenge in device engineering. Machine learning models often depend on extensive datasets, which are typically lacking specialized research domains such as extreme ultraviolet (EUV) radiation detection. Here, we demonstrate Cross-Spectral Response Prediction framework that leverages existing visible (UV) photoresponse data to predict much more efficient material’s performance under EUV radiation. Our predictive model, based Extremely Randomized Trees, correlates physical descriptors across spectral regions using comprehensive dataset of 1385 samples. Through this approach, identified promising α-MoO₃, ReS₂, Bi₂Te₃, SnO₂, achieving giant responsivities 15 40 A/W, exceeding conventional silicon photodiodes by 800 times sensing applications. Monte Carlo simulations revealed double electron generation rates (~2×10⁶ electrons per million photons) compared silicon, experimental validation confirming the effectiveness our prediction accelerating discovery other high performing diverse

Язык: Английский

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Highlights of mainstream solar cell efficiencies in 2024

Frontiers in Energy, Год журнала: 2025, Номер unknown

Опубликована: Янв. 30, 2025

Язык: Английский

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