A team of researchers has unveiled a powerful imaging technique that captures a full-dimensional portrait of elusive trap states—defects that hinder the performance of perovskite solar cells. By combining scanning photocurrent measurement system (SPMS) with complementary tools like thermal admittance spectroscopy (TAS) and drive-level capacitance profiling (DLCP), the team produced detailed spatial and energy maps of these hidden imperfections. Leveraging these insights, they introduced a passivation strategy using sulfa guanidine molecules that dramatically improved device performance. The result: a record-breaking solar cell achieving 25.74% efficiency. This breakthrough not only unlocks a deeper understanding of device physics but also provides a practical pathway to next-generation solar technologies.

Researchers Dr. Heeyeon Kim and Dr. Yoonseok Choi from the High Temperature Electrolysis Laboratory at the Korea Institute of Energy Research (KIER, President Yi, Chang-Keun), in collaboration with Professor WooChul Jung from the Department of Materials Science and Engineering at Seoul National University, have successfully improved a catalyst used in dry reforming reactions* that produce energy from greenhouse gases.

A recent study has revealed that the potato glucose 6-phosphate transporter StGPT1, which resides in both chloroplasts and the endoplasmic reticulum (ER), plays a crucial role in defending plants against Phytophthora infestans, the pathogen responsible for late blight. 

Kiwifruit bacterial canker, caused by Pseudomonas syringae pv. actinidiae (Psa), is a devastating disease threatening global kiwifruit production. 

Graph-structured data are pervasive in the real-world such as social networks, molecular graphs and transaction networks. Graph neural networks (GNNs) have achieved great success in representation learning on graphs, facilitating various downstream tasks. However, GNNs have several drawbacks such as lacking interpretability, can easily inherit the bias of data and cannot model casual relations. Recently, counterfactual learning on graphs has shown promising results in alleviating these drawbacks. Various approaches have been proposed for counterfactual fairness, explainability, link prediction and other applications on graphs. To facilitate the development of this promising direction, in this survey, researchers categorize and comprehensively review papers on graph counterfactual learning. Researchers divide existing methods into four categories based on problems studied. For each category, they provide background and motivating examples, a general framework summarizing existing works and a detailed review of these works. Researchers point out promising future research directions at the intersection of graph-structured data, counterfactual learning, and real-world applications. To offer a comprehensive view of resources for future studies, researchers compile a collection of open-source implementations, public datasets, and commonly-used evaluation metrics. This survey aims to serve as a “one-stop-shop” for building a unified understanding of graph counterfactual learning categories and current resources.