Energy-efficient buildings are promising for sustainable development and energy consumption as per environmental, social, and economic criteria. Recently, researchers from Hanbat National University, and Kongju National University, Republic of Korea, have proposed polymer-dispersed liquid crystal-impregnated switchable thermochromic transparent woods that demonstrate excellent ultraviolet blocking performance for smart windows, promoting indoor illumination, privacy, and human health. The novel innovation can help pave the way for next-generation energy-efficient buildings.

Using muon spin rotation spectroscopy, researchers from Japan and Canada successfully captured the rapid conversion of an imidoyl radical into a quinoxalinyl radical occurring within nanoseconds. The technique enabled real time detection of a highly reactive aromatic heterocyclic radical generated during the isocyanide insertion reaction, using muonium as a molecular tracker. The discovery is expected to advance particle-driven radical chemistry—exploring functional properties and offering new strategies for molecular transformation reactions.

Machine learning is revolutionizing fundamental science by tackling long-standing mathematical challenges. A key example is the classification of topological phases of matter. While topological invariants have been essential for characterizing these phases, no single invariant works universally. This limitation has led to many phases, once considered trivial, being reclassified as topological over the years. The recent discovery of non-Hermitian band topology has intensified efforts to classify these new phases, yet existing invariants still fail to capture all their unique features. Now, researchers from Tongji University, the Chinese university of Hong Kong and Nanyang Technological University have developed a machine-learning algorithm that performs unsupervised classification of symmetry-protected non-Hermitian topological phases—without relying on any predefined topological invariants. The method can autonomously construct a topological periodic table, bypassing the need for advanced mathematics. The learning process also yielded a formula revealing how parity transformation affects periodicity. Additionally, the algorithm accounts for boundary effects, allowing investigation of how open boundaries influence the topological phase diagram. These findings establish a powerful unsupervised framework for identifying non-Hermitian topological phases, uncover previously hidden topological traits, and offer valuable insights for future theoretical and experimental work.