Retraction notice to “Reduced atmospheric sulfate enhances fine particulate nitrate formation in eastern China” [Sci. Total Environ. 898 (2023) 165303] - Liang Wen; Likun Xue; Can Dong; Xinfeng Wang; Tianshu Chen; Ying Jiang; Rongrong Gu; Penggang Zheng; Hongyong Li; Ye Shan; Yujiao Zhu; Yong Zhao; et al. (Science of The Total Environment, 2026)

AI misuse of retracted literature: A comparative study of ChatGPT4o, deepseek, and grok 3 in stem cell research - Lan Yao; Tianshu Gu; Xuexin Li; Yan Jiao; Minghui Li; J. Carolyn Graff; Yulan Li; Weikuan Gu (The Science of Nature, 2025)
DeepSeek and Grok 3 appear as strong competitors to AI models, particularly the widely accepted model, ChatGPT. The accuracy of the utilization of data in retracted scientific articles has proven to be a significant challenge for AI as an assistant in scientific research. It is critical to understand whether and how three AI models handle information from retracted articles when they answer scientific questions. We collected retracted articles and used AI models to generate questions and analyzed the answers. The answers were compared and evaluated among three AI models. Here we show that these three models utilized 84 out of 93 retracted articles in their answers about stem cells. ChatGPT4o retrieved 74 out of 93 (80%) articles and recognized the retract status for 46 (62%) of them. DeepSeek only found one retracted article and did not realize its retraction status. Grok 4 retrieved 69 (74%) articles and recognized the retraction status of 46 (67%) of them. In cases when the retracted articles were not identified, ChatGPT fabricated articles 5 times out of 19 (26%) for its answers. Grok 3 fabricated 15 articles out of 24 (63%) for its answers. In 82 times of 93 (88%) answers, DeepSeek fabricated the articles in various forms. The answering styles from ChatGPT4o, DeepSeek, and Grok 3 are characterized by accurate and straightforward, a tangential structure and guesswork, and comprehensive and detailed answers, respectively. Analysis with non-retracted articles revealed the similar patterns of these models. This study suggests that, while no model is perfect, DeepSeek performed the worst when facing in-depth scientific real-world challenges. Much improvement has to be made before any of these AI models become problem-free and valuable for scientists.

Opto-band tuning in a liquid crystal elastomer phononic crystal plate - Tianshu Liang; Ying Liu; Qingxiao Gu (Wave Motion, 2025)

Design of four-level phase-change memory Ge2Sb2Te5/Ge50Te50 bilayer films and study of their crystallization behaviors - Ling Liu; Han Gu; Qiyu Cai; Liqi Zhu; Jie Song; Weihua Wu; Zixin Wang; Tianshu Lai (Chemical Engineering Journal, 2025)

Green revolution gene drives adaptation of Arabidopsis to the extremely high altitude - Xing-Hui Hou; Yong-Chao Xu; Tianshu Sun; Yan-Bo Gong; Xin-Tong Li; Guang-Teng Jin; Yu-Tao Bian; Yi-Ni Liu; Juan Jiang; Xiao-Min Niu; Hongya Gu; Ya-Long Guo (Science China Life Sciences, 2025)

m6A methylation in myocardial tissue of septic mice analyzed using MeRIP/m6A-sequencing and RNA-sequencing - Xue Liang; Xiaotong Hu; Jiao Li; Boyang Zhang; Tianshu Gu; Hualing Wang; Mingzhong Zhang; Xiaodong Xia; Siyu Guan; Wenfeng Shangguan; Shuai Miao; Weiding Wang; et al. (Functional & Integrative Genomics, 2024)

Research on low-power driving fatigue monitoring method based on spiking neural network - Tianshu Gu; Wanchao Yao; Fuwang Wang; Rongrong Fu (Experimental Brain Research, 2024)

A Perspective on Evaluating Life Stage Differences in Drug Dosages for Drug Labeling and Instructions - Tianshu Gu; Guiying Wang; Edwin J. C. van den Oord; Emanuel Goldman; Chengyuan Yang; Ning Xie; Lan Yao; Cong-Yi Wang; Monica Jablonski; Kunal Ray; Fengxia Liu; Wensen Pan; et al. (The AAPS Journal, 2024)

RETRACTED: Reduced atmospheric sulfate enhances fine particulate nitrate formation in eastern China - Liang Wen; Likun Xue; Can Dong; Xinfeng Wang; Tianshu Chen; Ying Jiang; Rongrong Gu; Penggang Zheng; Hongyong Li; Ye Shan; Yujiao Zhu; Yong Zhao; et al. (Science of The Total Environment, 2023)