Scientists have pinpointed, for the first time, exactly when key oil- and gas-forming rocks developed in northwest China. By precisely dating tiny zircon crystals preserved in ancient volcanic ash, researchers built a high-resolution timeline for Carboniferous–Permian source rocks in the Junggar Basin and nearby regions. The study shows that these source rocks formed during three distinct time windows and that the shift from marine to land-based environments occurred at different times across the region. These findings resolve long-standing geological debates, support a step-by-step, “scissor-like” closure of the Paleo-Asian Ocean, and provide a crucial time guide for future energy exploration.

Digital Planet at The Fletcher School at Tufts University launches the American AI Jobs Risk Index, the first comprehensive framework ranking U.S. occupations, industries, metro areas, and states by their actual vulnerability — not just exposure — to AI-driven job displacement. The Index projects that 9.3 million jobs are at risk over the next 2–5 years, representing up to $757 billion in annual household income. Silicon Valley, the birthplace of AI, faces the highest displacement of any major metro. High-skill, high-income knowledge workers — writers, programmers, analysts — are at greatest risk. This has implications for local economies but will also have ground-shifting political consequences. The window for making preparations and pre-emptive action is narrow. Learn what can be done to anticipate and head off an economic and political disruption.

Self-driving laboratories (SDLs) powered by artificial intelligence (AI) are rapidly accelerating materials discovery, but can they also explain their results? Researchers from the Theory Department of the Fritz Haber Insitute, in collaboration with BASF, and BasCat – UniCat BASF JointLab, show that they can. Their new AI-driven strategy works hand-in-hand with SDLs to identify better catalysts while revealing the chemistry behind their performance. The approach was validated on the industrially crucial conversion of propane into propylene.

Lithium-ion batteries power everything from electric vehicles and portable electronics to grid-scale energy storage, thanks to their high energy density, lack of memory effect, and adaptability across temperature ranges. However, repeated charge-discharge cycles cause gradual capacity fade, eventually rendering the battery unusable when it drops below a critical threshold. Accurate prediction of remaining useful life (RUL)—the number of cycles left before this failure point—is essential for proactive battery management, preventing unexpected failures, optimizing replacement schedules, and reducing costs and safety risks in real-world applications.

Lithium-ion capacitors (LICs) bridge the performance gap between traditional lithium-ion batteries and supercapacitors, delivering superior power density, extended cycle life, and significantly higher energy density than conventional double-layer capacitors. These attributes position LICs as a compelling solution for demanding applications such as electric vehicle acceleration, regenerative braking in urban rail systems, wind power smoothing, smart grid stabilization, and uninterruptible power supplies. Their ability to charge in seconds makes them particularly attractive for high-power scenarios, yet rapid charging introduces a serious risk: lithium plating on the anode. This unwanted deposition of metallic lithium can lead to reduced efficiency, capacity fade, increased internal resistance, and in severe cases, dendrite formation that risks short circuits and thermal runaway. Until recently, no direct or precise method existed to monitor lithium plating specifically in LICs during high-rate charging, limiting the safe exploitation of their full potential.

As cities worldwide accelerate toward electrification and decarbonization, the convergence of distributed energy resources and electric mobility is reshaping the architecture of modern power systems. Distributed Generation (DG), encompassing technologies such as solar photovoltaics, wind turbines, and fuel cells, has emerged as a critical enabler for reducing transmission losses and enhancing energy resilience. At the same time, the rapid adoption of electric vehicles (EVs) is introducing unprecedented demand patterns, placing new stress on existing distribution networks. The challenge lies not merely in deploying these technologies, but in orchestrating their integration in a way that ensures grid stability, efficiency, and sustainability.

Porous piezoelectric ceramics exhibit strong potential for sensing weak mechanical stimuli. However, the intrinsic coupling between the piezoelectric charge coefficient (d₃₃) and dielectric constant (εᵣ) limits energy conversion efficiency. Here, a fully open, three-dimensionally interconnected PZT-based porous ceramic (3D-PPC) is developed to overcome this constraint. Despite an ultrahigh porosity of 92%, the material retains a high d₃₃ (~470 pC/N), while εᵣ is significantly reduced (~140), leading to a ~14-fold enhancement in g₃₃ (~380 × 10^-3 V·m/N). This performance arises from synergistic effects of heterogeneous stress/electric fields, multiscale domain structures, and defect engineering, demonstrating that 3D interconnected porosity actively modulates local polarization behavior.