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Chitosan-doped graphene oxide complementary memristor enabling 1T1R gate selector for sustainable electronics

Memristors are promising for next-generation non-volatile memory and neuromorphic computing due to resistive switching (RS) behavior. Here, we demonstrate a chitosan-doped graphene oxide memristor with complementary RS, high stability, and repeatability. Voltage sweeps (±6 V) reveal RS with SET (0.9, −0.7 V) and RESET (2.25, −2 V) transitions, achieving an ON/OFF ratio of ∼104. The device maintains consistent complementary resistive switching over 2000 cycles, confirming non-volatile memory functionality. Statistical analysis shows SET/RESET distributions centered at 0.97 V/2.1 V (positive bias) and −1.1 V/−2.3 V (negative bias), indicating reliable switching. Pulse studies (3–5.5 V) reveal dynamic current responses linked to oxygen vacancy-based conductive filaments (CFs). A mechanistic model attributes RS to Vo migration and CF growth/dissolution between electrodes, with HRS1/HRS2 showing polarity-dependent asymmetry. In addition, a 1T1R unit integrating the memristor with a ZnO transistor enables gate-tunable memory operations and selector-free control. This work advances bio-organic memristors for high-density storage and neuromorphic systems.

Why Texas is the hottest place for space right now

Backscattering of Li+ ions from MoS2: Probing charge transfer through experiment and theory

We present a combined experimental and theoretical study of charge exchange dynamics in low-energy Li+ collisions with a MoS2 surface, focusing on the neutralization of backscattered projectiles. Using low-energy ion scattering, we measure charge-state-resolved time-of-flight spectra for incident energies between 2.5 and 8.0 keV, under different scattering geometries and azimuthal orientations. The results reveal neutralization fractions ranging from 20% to 35% for projectiles scattered from Mo atoms, with a slight but reproducible increase with increasing energy. These values correspond exclusively to single binary collisions between Li and Mo atoms, with negligible formation of negative ions and no significant dependence on azimuthal or entrance/exit angles. The experimental results are interpreted using a time-dependent resonant charge transfer model based on the Anderson Hamiltonian in the infinite-U limit. The model incorporates the interaction of the Li 2s level with the Mo-projected local density of states, calculated within a bond-pair formalism. Theoretical predictions reproduce well the magnitude of the measured neutral fractions, although they underestimate their energy dependence at higher energies, an effect possibly related to the omission of excited-state channels such as the Li 2p level. A qualitative comparison with charge transfer involving sulfur atoms reveals consistent trends but a systematic overestimation by the model, likely due to reionization effects in complex multi-atom trajectories. These results demonstrate the importance of local electronic structure in charge exchange processes and highlight the need for extended models that include excited states and multi-site interactions.

Gully formation in cities is displacing tens of thousands of people

Functional regimes define soil microbiome response to environmental change

Abstract The metabolic activity of soil microbiomes has a central role in global nutrient cycles 1 . Understanding how soil metabolic activity responds to climate-driven environmental perturbations is a key challenge 2,3 . However, the ecological, spatial and chemical complexity of soils 4–6 impedes understanding how these communities respond to perturbations. Here we address this complexity by combining dynamic measurements of respiratory nitrate metabolism 7 with modelling to reveal functional regimes that define soil responses to environmental change. Measurements across more than 1,500 soil microcosms subjected to pH perturbations 8,9 reveal regimes in which distinct mechanisms govern metabolite dynamics. A minimal model with two parameters, biomass activity and growth-limiting nutrient availability, predicts nitrate utilization dynamics across soils and pH perturbations. Parameter shifts under perturbation reveal three functional regimes, each linked to distinct mechanisms: (1) an acidic regime marked by cell death and suppressed metabolism; (2) a nutrient-limited regime in which dominant taxa exploit matrix-released nutrients; and (3) a resurgent growth regime driven by exponential growth of rare taxa in nutrient-rich conditions. We validated these model-derived mechanisms with nutrient measurements, amendment experiments, sequencing and isolate studies. Additional experiments and meta-analyses suggest that functional regimes are widespread in pH-perturbed soils.

Non-collinear ferroelectric H2O ice VIII

We present a non-collinear ferroelectric phase of ice VIII (space group Iba2). This non-collinear ferroelectric phase should be an important base together with the anti-ferroelectric (I41/amd) and the ferroelectric (P42nm) ices to describe the structure of disordered dense H2O ice (ice VII). The enthalpy of Iba2 is slightly larger than that of I41/amd and slightly smaller than that of P42nm (by < 1 mRy/molecule). The other ices could exist at finite temperatures as the energy difference is very small. The Iba2 ice VIII coexisting with the I41/amd one can explain some anomalies observed in previous experiments.

Dynamic and thermodynamic origins of non-equilibrium phase transitions in infectious disease networks

Understanding the mechanisms driving phase transitions in epidemic dynamics is essential for predicting and controlling infectious disease outbreaks. In this study, we apply the landscape and flux framework from nonequilibrium statistical physics to investigate the physical origins of bifurcations, or nonequilibrium phase transitions, in adaptive epidemic networks. Using a SIRS model, we systematically examine how variations in the rewiring rate (representing individuals’ behavioral responses to avoid infection) and the average node degree (indicating the population’s contact density) reshape the topography of the system’s potential landscape and alter barrier heights, thereby triggering transitions between bistable and monostable regimes. Our findings reveal that rotational flux acts as a nonequilibrium driving force underlying these transitions, while the entropy production rate quantifies the associated thermodynamic cost. In addition, we identify critical slowing down, time irreversibility, and flickering frequency as effective early warning indicators of critical transitions when the rewiring rate or the average node degree of the network changes. These results offer quantitative tools and potential strategies for anticipating abrupt public health crises.

Five highlights from skin research

Water under hydrophobic confinement: entropy and diffusion

The properties of liquid water are known to change drastically in confined geometries. A most interesting and intriguing phenomenon is that the diffusion of water is found to be strongly enhanced by the proximity of a hydrophobic confining wall relative to the bulk diffusion. We report a molecular dynamics simulation using a classical water model investigating the water diffusion near a non-interacting smooth confining wall, which is assumed to imitate a hydrophobic surface, revealing a pronounced diffusion enhancement within several water layers adjacent to the wall. We present evidence that the observed diffusion enhancement can be accounted for, with a quantitative accuracy, using the universal scaling law for liquid diffusion that relates the diffusion rate to the excess entropy. These results show that the scaling law, which has so far only been used for the description of the diffusion in simple liquids, can successfully describe the diffusion in water. It is shown that the law can be used for the analysis of water dynamics under nanoscale hydrophobic confinement, which is currently a subject of intense research activity.

Skin: preserving the health of a multi-talented organ

Adaptations for stealth in the wing-like flippers of a large ichthyosaur

Abstract With their superficially shark-like appearance, the Mesozoic ichthyosaurs provide a classic illustration of major morphological adaptations in an ancestrally terrestrial tetrapod lineage following the invasion of marine habitats1–3. Much of what is known about ichthyosaur soft tissues derives from specimens with body outlines4–6. However, despite offering insights into aspects of biology that are otherwise difficult to envisage from skeletal evidence alone (such as the presence of a crescentic fluke), information on their soft parts has hitherto been limited to a taxonomically narrow sample of small- to dolphin-sized animals2,4–6. Here we report the discovery of a metre-long front flipper of the large-bodied Jurassic ichthyosaur Temnodontosaurus, including unique details of its soft-tissue anatomy. In addition to revealing a wing-like planform, the fossil preserves a serrated trailing edge that is reinforced by novel cartilaginous integumental elements, herein denominated chondroderms. We also document chordwise-parallel skin ornamentations and a protracted fleshy distal tip that presumably acted like a flexible winglet in life. By integrating morphological and numerical data, we show that the observed features probably provided hydroacoustic benefits, and conclude that the visually guided7,8 Temnodontosaurus relied on stealth while hunting in dim-lit pelagic environments. This unexpected combination of control surface modifications represents a previously unrecognized mode of concealment, and underscores the importance of soft-tissue fossils when inferring aspects of palaeoethology and predator–prey palaeoecology.

Incorporating local step-size adaptivity into the no-U-turn sampler using Gibbs self-tuning

Adapting the step size locally in the no-U-turn sampler (NUTS) is challenging because the step-size and path-length tuning parameters are interdependent. The determination of an optimal path length requires a predefined step size, while the ideal step size must account for errors along the selected path. Ensuring reversibility further complicates this tuning problem. In this paper, we present a method for locally adapting the step size in NUTS that is an instance of the Gibbs self-tuning (GIST) framework. Our approach guarantees reversibility with an acceptance probability that depends exclusively on the conditional distribution of the step size. We validate our step-size-adaptive NUTS method on Neal’s funnel density and a high-dimensional normal distribution, demonstrating its effectiveness in challenging scenarios.

Genome doubling fuels ovarian cancer evolution and immune dysregulation

Optimizing machine learning interatomic potentials for hydroxide transport: Surprising efficiency of single-concentration training

We investigate the transferability of machine learning interatomic potentials across concentration variations in chemically similar systems, using aqueous potassium hydroxide solutions as a case study. Despite containing identical chemical species (K+, OH−, and H2O) across all concentrations, models fine-tuned on specific KOH concentrations exhibit surprisingly poor transferability to others, with force prediction errors increasing dramatically from 30 meV Å−1 (at training concentration) to 90 meV Å−1 (at very different concentrations). This reveals a critical limitation when applying such models beyond their training domain, even within chemically homogeneous systems. We demonstrate that strategic selection of training data can substantially overcome these limitations without requiring extensive computational resources. Models fine-tuned on intermediate concentrations (6.26 mol l−1) exhibit remarkable transferability across the entire concentration spectrum (0.56–17.89 mol l−1), often outperforming more computationally expensive models trained on multiple concentration datasets. This approach enables accurate simulation of hydroxide transport dynamics across varying electrolyte conditions while maintaining near-quantum accuracy. Our simulations further reveal the emergence of hydroxide–hydroxide hydrogen bonding at high concentrations—a phenomenon not explicitly represented in dilute training data but successfully captured by our intermediate-concentration model. This work establishes practical guidelines for developing broadly applicable machine learning force fields with optimal transferability, challenging the assumption that diverse training datasets are always necessary for robust performance in similar chemical environments.

Analyzing the internal interface in localized high-concentration electrolytes

We present molecular dynamics simulations on localized high-concentration electrolytes (LHCE) based on the conducting salt lithium bis(fluorosulfonyl)imide (LiFSI) or lithium bis(trifluoromethanesulfonyl)imide dissolved in the solvent 1,2-dimethoxyethane and diluted to two different degrees with the diluent 1,1,2,2-tetrafluoroethyl-2,2,3,3-tetrafluoropropyl ether. Due to the immiscibility of the conducting salt phase (salt + solvent) and the diluent phase, LHCEs feature a complex microstructure of two phases forming an internal interface. In this study, we not only investigate the lithium coordination structure in the conducting salt phase but also the size and composition of its interface to the diluent phase by Voronoi tessellations. Furthermore, we investigate the influence on the ion transport by evaluating Onsager coefficients. We show that an LHCE containing the surface-active anion TFSI– creates an anion-rich internal interface, leading to enhanced ion dissociation and anticorrelated ion movement. On the other hand, the smaller FSI– anion with a more localized charge distribution and less amphiphilic character shows no enrichment at the internal interface, but rather a depletion. By increasing LiFSI concentration, we even observe a solvent-rich internal interface due to a large and branched Li–anion network. Furthermore, the less diffuse interface and enlarged Li–anion network lead to lower ion–ion anticorrelations and a stronger convective flux of the conducting salt phase, which is compensated by a flux of the diluent phase, especially in the higher concentrated LiFSI based LHCE.

Influence of vibrational motion and temperature on interatomic Coulombic electron capture

Interatomic Coulombic electron capture (ICEC) is an environment-mediated process in which a free electron attaches to a species by transferring excess energy to a neighbor. While previous theoretical investigations assumed fixed nuclei, recent studies indicate that nuclear dynamics significantly influences the ICEC process. In this work, we incorporate the vibrational motion into an analytical model of the ICEC cross section, including both energy and electron transfer. To validate this approach, we compare the results to the adiabatic-nuclei approximation based on fixed-nuclei ab initio R-matrix calculations. We apply our theory to the helium–neon dimer, which is ideal for studying diverse dynamical effects. We show that while vibrational dynamics can slightly reduce ICEC efficiency, ICEC remains dominant over photorecombination and can trigger dimer dissociation. Accounting for the nuclear motion also enables to describe the broadening of the electron spectrum and enables evaluation of temperature-dependent cross sections—capabilities beyond the reach of fixed-nuclei approaches.

Acne vaccines could offer robust defence

Exploring the role of chaos in model recollision processes

The physics of particle recollisions offers a window into the complex dynamics of interactions between charged particles and external fields. While simple classical models often describe these recollisions by focusing on the motion driven by an external field alone, e.g., the three-step model in high harmonic generation, this assumption excludes the possibility of chaotic behavior. In this work, we explore how chaotic motion emerges in recollision processes by including the strength of the Coulomb potential as a parameter. Through a continuous scan of system parameters, we uncover the transition from regularity to chaos. Interestingly, we find a transition from regular to chaotic to regular motion as a function of the 2D scan of Coulomb strength and field strength. In addition, scanning over the initial phase of the driving field allows us to identify the sensitive dependence on initial conditions characteristic of chaotic motion. Our findings reveal that the system can exhibit chaotic dynamics on timescales much longer than the initial recollision.

Influence of excitonic coupling, static disorder, and coherent dynamics in action-2D electronic spectroscopy of a molecular dimer model

We investigate the spectral features of Action-2D Electronic Spectroscopy (A-2DES) using a molecular dimer model across different regimes of excitonic coupling. By explicitly including a second-excited state for each chromophore, we simulate A-2DES spectra ranging from the non-interacting limit to the strong-coupling case, focusing on the significance of cross peaks. While for weak excitonic coupling, cross peaks can be understood as the incoherent mixing of linear signals of the two chromophores, these features reflect excitonic delocalization as the coupling increases. We highlight that A-2DES offers enhanced sensitivity to coherent excited-state dynamics, particularly in the intermediate-coupling regime, where it provides higher contrast compared to its coherent-detected counterpart. Finally, we show the different influences of static disorder on the line shapes of diagonal and cross peaks. Notably, since cross peaks exhibit rephasing capability for increasing excitonic coupling, the contribution of incoherent mixing becomes less significant in inhomogeneous samples. These findings support the potential of A-2DES for investigating excitonic dynamics in small multi-chromophoric systems.

Mapping urban gullies in the Democratic Republic of the Congo

Abstract Large urban gullies cause damage in many tropical cities across the Global South1,2. They can result from inappropriate urban planning and insufficient infrastructure to safely store and evacuate rainfall in environments that are already highly sensitive to soil erosion1,3,4. Although they can cause large destruction and societal impacts such as population displacement1,2,5, the magnitude of this geo-hydrological hazard remains poorly documented and understood6,7. Here we provide an assessment of the extent and impact of urban gullies at the scale of the Democratic Republic of the Congo (DRC). Through mapping, we identify 2,922 urban gullies across 26 cities. By combining their formation and growth rates with population density data8, we estimate that around 118,600 people (uncertainty range: ± 44,400 people) have been displaced by urban gullies over the period 2004–2023. We find that average displacement rates increased from about 4,650 persons yr−1 (pre-2020) to about 12,200 persons yr−1 (post-2020). Between 2010 and 2023, the number of people living in the potential expansion zone of urban gullies doubled from 1.6 (±0.6) to 3.2 (±1.3) million, with more likely to be exposed due to urban sprawl9,10 and climate change11. We suggest that there is a need for tools and strategies to prevent and mitigate this hazard.