Evidence map›Paper›PMID 42504069›Full record

ArticleSmall methods2026

Large Language Model Guided Discovery of Hole Transport Layer Dopants for Efficient and Stable Perovskite Photovoltaics.

Jiazheng Wang, Xinxin Xu, Qiang Lou, Hongye Liu, Zhengjie Xu, Chiuyung Chen, Qing Han, Hao Zhang, Jing Guo, Guibo Luo and 2 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Small methods, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

12 authors.

Jiazheng WangGuangdong Provincial Key Laboratory of In-Memory Computing Chips, School of Electronic and Computer Engineering, Peking University, Shenzhen, China.ORCID https://orcid.org/0009-0003-6836-8186
Xinxin XuGuangdong Provincial Key Laboratory of In-Memory Computing Chips, School of Electronic and Computer Engineering, Peking University, Shenzhen, China.
Qiang LouGuangdong Provincial Key Laboratory of In-Memory Computing Chips, School of Electronic and Computer Engineering, Peking University, Shenzhen, China.
Hongye LiuGuangdong Provincial Key Laboratory of In-Memory Computing Chips, School of Electronic and Computer Engineering, Peking University, Shenzhen, China.
Zhengjie XuSchool of Mechanical and Electrical Engineering, Guangzhou Institute of Technology, Guangzhou, China.ORCID https://orcid.org/0000-0002-3593-4327
Chiuyung ChenGuangdong Provincial Key Laboratory of In-Memory Computing Chips, School of Electronic and Computer Engineering, Peking University, Shenzhen, China.
Qing HanGuangdong Provincial Key Laboratory of In-Memory Computing Chips, School of Electronic and Computer Engineering, Peking University, Shenzhen, China.
Hao ZhangGuangdong Provincial Key Laboratory of In-Memory Computing Chips, School of Electronic and Computer Engineering, Peking University, Shenzhen, China.ORCID https://orcid.org/0009-0002-6736-3159
Jing GuoSchool of Physics and Electronic Engineering, Shanxi Normal University, Taiyuan, China.
Guibo LuoGuangdong Provincial Key Laboratory of In-Memory Computing Chips, School of Electronic and Computer Engineering, Peking University, Shenzhen, China.
Yuanyuan HuState Key Laboratory of Power Semiconductor and Integration Technology, Changsha Semiconductor Technology and Application Innovation Research Institute, College of Semiconductors (College of Integrated Circuits), Hunan University, Changsha, China.ORCID https://orcid.org/0000-0001-8511-1401
Hang ZhouGuangdong Provincial Key Laboratory of In-Memory Computing Chips, School of Electronic and Computer Engineering, Peking University, Shenzhen, China.ORCID https://orcid.org/0000-0002-0472-9515

Funding

Department of Education of Guangdong Province 2025KQNCX221Guangdong Science and Technology programme 2024B1212020002Guangzhou Municipal Education Bureau 2025XGYS016Science, Technology and Innovation Commission of Shenzhen Municipality GJHZ20240218113959009Science, Technology and Innovation Commission of Shenzhen Municipality KJZD20230923115005009
6 · The paper itself

Abstract

Achieving high efficiency and long-term stability in n-i-p perovskite solar cells (PSCs) remains constrained by the hole transport layer (HTL) dopant chemistry. The most commonly used dopant for 2,2',7,7'-tetrakis(N,N-di-4-methoxyphenylamino)-9,9'-spirobifluorene (Spiro-OMeTAD), typically based on lithium bis(trifluoromethanesulfonyl)imide, enables state-of-the-art power conversion efficiency (PCE) but often sacrifices thermal and environmental robustness due to hygroscopicity, ionic migration, and reduced glass-transition temperature. Here, a HTL-dopant-focused large language model (LLM) framework is constructed to mine the literature at scale. Using a corpus of over 70 000 publications for retrieval-guided learning, the model identifies trityl tetrakis(pentafluorophenyl) borate (TrTPFB) as an effective p-dopant that improves hole transport in Spiro-OMeTAD, while also improving the morphology and hydrophobicity of the HTL film. With optimized TrTPFB doping concentration, the champion lithium-free Spiro-OMeTAD based device reaches a PCE of 24.13%, and retains 92.67% and 85.82% of its initial PCE after 900 h thermal aging at 65°C with 30% RH and at 85°C in N

Indexed as

dopantshole transport layerlarge language modelperovskite solar cellsthermal stability

Identifiers

PMID42504069
PMCPMC13505655

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.