From lab to product: charting the future of ionic thermoelectrics, a versatile platform for harvesting and using low-grade heat
Peer-Reviewed Publication
Updates every hour. Last Updated: 28-Oct-2026 03:17 ET (28-Oct-2026 07:17 GMT/UTC)
Ionic thermoelectrics (i-TEs) convert temperature differences into electricity, taking forms from liquids and gels to polyelectrolytes and porous solids. A new review in National Science Review takes a bird's-eye view—from the physics and chemistry of a single electrolyte to the engineering of commercial products—and lays out a material–device–system design framework. The authors conclude that reaching commercial products demands concerted, cross-disciplinary efforts in standardization, scalable manufacturing, reliability, and sustainability—not merely ever-higher performance metrics.
Near‑Earth asteroid impacts pose a major threat that could lead to the destruction of human civilization, having already caused catastrophic environmental changes and mass extinctions multiple times in history. In recent years, asteroids with diameters ranging from tens to hundreds of meters have frequently made close flybys of Earth, and a large number of large‑sized asteroids remain undiscovered, with warning times often only a few days to weeks. For large‑sized asteroids exceeding 100 meters in diameter, or even kilometer‑scale ones, traditional kinetic impact or long‑term force deflection methods offer limited energy and cannot achieve effective deflection within short timeframes. Using the enormous energy generated by nuclear detonation to directly destroy or rapidly deflect the asteroid's orbit is the most effective, and in extreme cases the only feasible, approach for dealing with large asteroids or those with short warning times. However, existing research on detonation defense has mostly focused on the direct rendezvous impact mode, in which the impact and detonation positions cannot be autonomously selected and energy coupling is relatively weak. Moreover, there is a lack of systematic analysis of the capability coverage and overall effectiveness of different defense modes, severely constraining the optimization of engineering designs.
Since the discovery of the Hall effect in 1879, it has given rise to a series of landmark advances in condensed matter physics. These include the quantum Hall effect and the fractional quantum Hall effect, recognized with Nobel Prizes in 1985 and 1998, respectively, as well as the experimental realization of the quantum anomalous Hall effect in 2013, which demonstrated how the interplay between topology and ferromagnetism can produce dissipationless edge currents.
Despite this long history, research on the Hall effect—particularly the anomalous Hall effect—has largely concentrated on the Hall resistance, derived from the transverse voltage. By contrast, the magnetoresistance, obtained from the longitudinal voltage, is important for helping scientists understand exotic quantum phenomena such as the giant magnetoresistance effect. However, the magnetoresistance has received far less attention in past decades largely because of the complexity for interpreting the data. Only in recent years has a theoretical model—a two-band framework incorporating anomalous Hall conductivity—been developed that can effectively capture its behavior. Even so, the resulting expression for magnetoresistance remains complicated, limiting deeper understanding.
Writing in National Science Review, a team from Nanjing University now offers a solution. Using machine learning, the researchers analyzed over two million magnetoresistance curves generated from the above-mentioned two-band model. By applying the mean-shift algorithm, they classified the curves into 13 distinct magnetoresistance states and established topological networks that describe transitions among them. These networks make it possible to design transition paths for switching magnetoresistance states by tuning selected electronic parameters. Experimental magnetoresistance measurements on a gated ferromagnetic material confirmed the reliability of the obtained phase diagrams. This machine-learning-assisted approach to high-dimensional data processing provides a powerful new methodology for investigating transport phenomena.
Human activity and climate change are estimated to increase the amount of seabed methane leakage. Much of the methane could be consumed by microbes, yet it is unclear how quickly such communities establish. Through six-year seabed monitoring, researchers found that an animal-microbe “biofilter” removing up to100% of the dissolved methane can develop in just 2–3 years, not the decades once thought. Fast-growing methane-oxidizing microbes feed burrowing animals, which churn the seafloor like tiny gardeners to help the microbes feast on the greenhouse gas. This discovery improves our understanding of seafloor methane removal capacity, which is critical to assess the effects of global warming and anthropogenic disturbance in the deep sea.
A recent study published in National Science Review reveals that soil fungi with broad geographic distributions play a disproportionately important role in carbon decomposition. By combining nationwide field surveys across China, and DNA stable isotope probing experiments, researchers found that widespread saprotrophic fungi, not locally diverse but narrowly distributed species, are the main active decomposers of plant residues. The findings establish occupancy as a key biogeographic property linking microbial diversity to ecosystem functioning.
Researchers have built the first photonic computing chip that simultaneously exploits 32 wavelengths, 3 spatial modes, and 2 polarizations—192 parallel channels in total. The chip supports dynamically reconfigurable convolution kernels up to 13×13, enabling multi‑scale feature extraction and structured noise suppression. It achieves an experimental throughput of 15.36 TOPS, paving the way for high‑precision optical AI in computer vision and biomedical imaging.
Autophagy plays a cell-specific, bidirectional role in neuropathic pain-protective in some contexts but potentially detrimental in others. This review maps the complex regulatory network across neurons, astrocytes, and microglia, and critically evaluates therapeutic strategies targeting this cellular process, offering a roadmap for developing precision treatments.
As humanity enters the era of long‑term crewed deep‑space exploration, understanding the brain’s adaptive mechanisms to extreme environments such as microgravity and radiation has become a critical issue in space medicine. Previous studies using magnetic resonance imaging have revealed structural brain plasticity after spaceflight, including ventricular expansion and changes in grey matter volume. However, knowledge of dynamic changes at the neurophysiological level remains limited, and most existing evidence comes from group‑level analyses of long‑duration missions (e.g., six months), leaving the specific neurofunctional impact of short‑duration missions on individuals unclear. Conventional neuroimaging equipment is bulky and cannot be used in orbit. In contrast, electroencephalography (EEG) is portable, low‑cost, and repeatable, and has shown sensitivity in neurological conditions such as concussion, but its application in the extreme spaceflight environment has not been reported. Therefore, validating the feasibility of portable EEG technology for monitoring individual brain function before and after short‑duration spaceflight holds significant practical value for building future in‑orbit brain health assessment systems in deep‑space exploration.
Homeostatic Medicine, as an emerging integrative discipline, is driving a profound shift in medical paradigms from the traditional "eradication of pathogenic factors" to the "restoration of inherent homeostasis" by decoding the core mechanisms of the body's self-regulation. This discipline redefines the essence of health and disease, offering a novel theoretical framework and clinical intervention strategies for disease prevention and treatment.
Researchers from Chongqing Medical University discovered that excessive fibrin deposition drives chronic gum inflammation in diabetes by disrupting immune-epithelial crosstalk. By utilizing an engineered peptide (377P) to block fibrin-macrophage interactions, the team successfully suppressed inflammation, restored the mucosal barrier, and promoted alveolar bone healing in diabetic mice. This study identifies fibrin as a druggable target for diabetic periodontitis and potentially other systemic diabetic complications.