Fengqi You

Jan 2, 2025 | pubs.rsc.org | Fengqi You

Perspectives for sustainability analysis of scalable perovskite photovoltaics† Halide perovskite photovoltaics (PVs) are poised to become a critical high-efficiency renewable energy technology in the fight against climate change. This perspective aims to ensure the viability of perovskite PV as a sustainable technology by focusing on key areas such as end-of-life management and sustainability analysis.

Oct 23, 2024 | pubs.rsc.org | Fengqi You |Xueyu Tian |Samuel D. Stranks |Jinsong Huang

Perspectives for Sustainability Analysis of Scalable Perovskite Photovoltaics Halide perovskite photovoltaics (PVs) are poised to become a critical high-efficiency renewable energy technology in the fight against climate change. This perspective aims to ensure the viability of perovskite PV as a sustainable technology by focusing on key areas such as end-of-life management and sustainability analysis.

Oct 9, 2024 | dx.doi.org | Xiang Zhao |Fengqi You

This publication is Open Access under the license indicated. Learn MoreAddition/CorrectionOctober 9, 2024*Environmental Science & TechnologyCite this: Environ. Sci. Technol. 2024, XXXX, XXXClick to copy citationCitation copied!correction. This publication is licensed under CC-BY-NC-ND 4.0 . You are free to share (copy and redistribute) this article in any medium or format within the parameters below:Creative Commons (CC): This is a Creative Commons license.

Sep 9, 2024 | nature.com | Fengqi You

AbstractPlant factories with artificial lighting (PFALs) can boost food production per unit area but require resources such as carbon dioxide and energy to maintain optimal plant growth conditions. Here we use computational modelling and artificial intelligence (AI) to examine plant–environment interactions across ten diverse global locations with distinct climates.

Aug 14, 2023 | nature.com | Fengqi You

AbstractComputer-aided design of novel molecules and compounds is a challenging task that can be addressed with quantum computing (QC) owing to its notable advances in optimization and machine learning. Here, we use QC-assisted learning and optimization techniques implemented with near-term QC devices for molecular property prediction and generation tasks.