Vinayak P. Dravid

Efficient and durable catalysts for the acidic oxygen evolution reaction (OER) are critical for hydrogen production but remain limited by reliance on scarce iridium (Ir). We present an ultralow‐loading Ir catalyst (3 at%, − ² ) integrated within a hollandite MnO ₂ framework, which lowers the overpotential at 10 mA cm ^{− 2} from 580 mV (undoped α‐MnO ₂ ) to 303 mV and delivers an Ir mass activity an order of magnitude higher than commercial IrO _x . Varying pH and potential, we combine ICP‐MS dissolution measurements with x‐ray absorption spectroscopy, electron energy loss spectroscopy, and 4D scanning transmission electron microscopy. Dissolution is modest, with at least 79% of the Ir and 76% of the Mn remaining after 6 h under applied potential, and Ir remains oxidized and associated with the hollandite framework. Mn, in contrast, undergoes pH‐ and potential‐dependent reduction and local structural disorder. Notably, the strongest Mn reconstruction (pH 0, 1.65 V) coincides with moderate dissolution, whereas the greatest Ir and Mn loss (pH 0, 1.60 V) occurs where the residual material is less reconstructed. Net dissolution therefore does not report on local structural change, highlighting the need to evaluate dissolution, reconstruction, and oxidation state together when designing and assessing Ir‐efficient mixed‐metal oxide catalysts.

The structural behavior of CsTaS3 was investigated alongside that of the new heteroanionic compound CsTaS2.16Cl0.84, synthesized via the reaction of Ta and S in a CsCl flux. Single-crystal X-ray diffraction reveals that both compounds are isostructural, crystallizing in the centrosymmetric hexagonal space group P63/mmc with split Ta positions along the face-sharing octahedral chain direction. Pair distribution function analysis of both compounds, supported by STEM imaging of CsTaS3, identifies the local intrachain displacement motif as cooperative Ta sliding rather than alternating Ta−Ta dimerization. While X-ray absorption near-edge spectroscopy (XANES) confirms the decrease of the average oxidation state in CsTaS2.16Cl0.84, the same structural distortion persists, albeit now with a reduced magnitude. Both materials are semiconducting, and Cl incorporation increases the band gap from ∼1.00 eV in CsTaS3 to ∼1.12 eV in the heteroanionic phase. Single-crystal detectors fabricated from CsTaS2.16Cl0.84 exhibit high resistivity, which effectively suppresses dark current and enhances signal-to-noise performance. These devices deliver an X-ray sensitivity of 7470 μC Gyair−1 cm−2, demonstrating efficient charge generation and collection under irradiation.

Vanadium-based thiophosphates in the MPQ3/M2P2Q6 family provide an unusual platform for coupling mixed-metal valence, metal vacancies, and 2D magnetism, but access to phase-pure multimetal solid solutions with verified chemical homogeneity remains limited. Here, we show that multication alloying provides a route to incorporating V into layered thiophosphates while retaining the mixed-valent, vacancy-containing character of V2−xP2S6. This approach yields Fe1.25V0.6P2S6, Ni1.25V0.6P2S6, Co1.25V0.6P2S6, and high-entropy V-containing compositions. Single-crystal X-ray diffraction (scXRD) and electron energy-loss spectroscopy (EELS) mapping confirm formation of layered single-phase alloys and chemical uniformity both across the surface and through the flake thickness. Optical measurements show semiconducting band gaps spanning ∼1.33−1.66 eV, while temperature-dependent transport reveals markedly enhanced conductivity and low activation energies (∼0.2−0.3 eV) relative to conventional divalent TM2P2S6 analogs. Oxidation-state mapping indicates robust mixed-valent V2+/V3+ character consistent with defect-assisted hopping and self-doping linked to vanadium vacancies, likely causing enhanced conductivity. Magnetic measurements further show composition-dependent behavior, consistent with magnetic frustration and glassy components in Fe1.25V0.6P2S6 and (VMnFeCoNi)2P2S6, highlighting the role of multimetal disorder in shaping exchange interactions. Together, these results establish a route to phase-pure, conductive V-containing thiophosphate alloys and expand the tunable design space for coupled transport and magnetism in layered chalcophosphates.

Magnetic tunnel junctions based on ferromagnets are canonical devices in spintronics, with wide-ranging applications in data storage, computing and sensing. They simultaneously exhibit mechanisms for electrical detection and control of magnetic order through the tunnelling magnetoresistance (TMR)^1-5 and spin-transfer torque^6-9 effects, respectively. It was long assumed that neither of these effects could be sizeable in all-antiferromagnetic tunnel junctions (AATJs), considering that they have no net magnetization. However, it was recently shown that AATJs based on chiral antiferromagnets do exhibit TMR due to their non-relativistic momentum-dependent spin polarization and cluster magnetic octupole moment, which are manifestations of their spin-split band structure^10-13. However, the reciprocal effect, that is, the antiferromagnetic counterpart of spin-transfer torque, has been assumed non-existent due to the total electric current being spin-neutral. Here we report nanoscale AATJs that display this reciprocal effect, which we term octupole-driven spin-transfer torque (OTT). We demonstrate current-induced OTT switching of PtMn₃|MgO|PtMn₃ AATJs, with a TMR value of 363% at room temperature and switching current densities of the order of 10 MA cm^-2. Our theoretical modelling explains the origin of OTT in terms of the imbalance between intra- and inter-sublattice spin currents across the AATJ, and, equivalently, in terms of the non-zero net cluster octupole polarization of each PtMn₃ layer. This work establishes a new materials platform for antiferromagnetic spintronics and provides a pathway towards deeply scaled magnetic memory and room-temperature terahertz technologies.

The design of novel materials hinges on the understanding of structure–property relationships. However, in recent times, our capability to synthesize a large number of materials has outpaced our speed of characterizing them, and thorough crystal structure analysis of newly synthesized samples remains a bottleneck in high-throughput nanomaterials discovery. Thus, rapid methods for crystal symmetry determination that can analyze many material samples within a short time frame are especially needed. Kikuchi diffraction in the scanning electron microscope (SEM) is a promising technique for this due to its sensitivity to dynamical scattering, which may provide space group information. In this study, we train neural networks to classify the space group from cubic Kikuchi patterns to enable its use for high-throughput symmetry classification of novel material samples. Due to the low number of material phases in the experimental training data, an unsupervised deep learning-based domain adaptation method was utilized to train neural networks on both simulated and experimental data. This helped to remedy the low diversity of the experimental training data. Additionally, instead of predicting the true space group, we relabel the data based on space group information that is realistically obtainable given physical limitations of Kikuchi diffraction, which enables our models to achieve accuracy scores higher than 95% for simulated and experimental data. This suggests that adapting to limitations imposed by diffraction physics is crucial for developing working models for symmetry classification.

Excess phosphate in water bodies leads to eutrophication, threatening water quality and wasting a critical, nonrenewable resource. Wastewater is a major untapped source of phosphate, but current methods have limited removal and recovery efficiency at realistic concentrations and generate substantial waste. Iron oxide-coated cellulose sponge adsorbents offer a sustainable approach for phosphate recovery, but the effects of the background ionic composition in wastewater on phosphate adsorption are not fully understood. This study investigates phosphate adsorption on an iron oxide-coated cellulose sponge under varying ionic conditions. The presence of 0.2 mM Ca2+ and 0.01 mM Cu2+ increased the fitted Langmuir maximum adsorption capacity of phosphate by 32% and 70%, respectively. In contrast, 0.32 mM NO3– and 1.04 mM SO42– reduced phosphate adsorption by 12% and 10% (not statistically significant), respectively. SEM-EDS, XPS, and ToF-SIMS analyses, together with ζ potential measurements, indicated that Ca2+ and Cu2+ enhanced phosphate adsorption through cation-mediated interactions and surface charge modification. These findings elucidate how background ionic composition influences phosphate adsorption and adsorbent performance, providing mechanistic insights for phosphate removal and recovery from wastewater.

Superconducting qubits show great promise for the realization of fault-tolerant quantum computing, but lossy, amorphous dielectrics limit current technology. Identifying highly crystalline and stoichiometric dielectrics with intrinsically low microwave loss is therefore a central materials challenge, yet experimentally validated platforms remain scarce. This work integrates a crystalline dielectric into a heteroepitaxial TiN/γ-Al2O3/TiN trilayer grown via pulsed laser deposition. Correlative high-resolution imaging, diffraction, and spectroscopy measurements confirm the single-crystal quality and chemical integrity of all layers, with minimal defects and limited anion interdiffusion across the oxide–nitride interfaces. Using microwave lumped-element resonators with parallel-plate capacitors, this work establishes an experimentally measured upper bound for the dielectric loss of epitaxial γ-Al2O3, which has a low intrinsic two-level system loss of δTLS0 = (2.8 ± 0.1) × 10–5. These results establish heteroepitaxial oxides on transition metal nitrides as an attractive materials platform for superconducting quantum circuits, particularly for integration into compact device architectures such as merged-element transmons and microwave kinetic inductance detectors.

Electron-beam irradiation limits atomic-resolution characterization of beam-sensitive hybrid materials, yet quantitative models that connect in situ spectroscopy to dose-dependent degradation remain scarce. Here, we use a physics-informed neural network (PINN) to model beam-induced spectral evolution in MIL-101(Fe) from an in situ low-loss electron energy-loss spectroscopy (EELS) dose series. Each spectrum is reduced to fixed-window low-loss descriptors, ñeff,j(Φ)=∫WjS(E,Φ)dE, evaluated over nominal π–π*, C–C, C–O, and M–O windows. These descriptors are relative window-integrated low-loss spectral areas, not absolute f-sum-rule effective electron numbers. For each spectral channel, a latent integrity variable Ci(Φ) obeys the same uncoupled power-law degradation equation in normalized dose space, dCi/dϕ=−kiCipi, regularized by monotonicity, boundedness, and a single hierarchy prior kC–O ≥ kC–C. Applied to nine dose frames spanning 152–1368 e−/Å2, the ensemble PINN identifies C–O and C–C as the most strongly dose-sensitive linker-associated channels, with half-integrity thresholds of ∼1.0 × 103 e−/Å2. The 1–3 eV π–π*-labeled window increases with dose and is, therefore, interpreted as a mixed low-energy response, likely involving oscillator-strength redistribution rather than direct monotonic loss of a single bond population. The framework provides a dose-dependent, spectroscopy-constrained description of MOF degradation while explicitly stopping short of chemical-mechanism assignment: fixed-window low-loss EELS descriptors are phenomenological and require additional chemical-state validation before they can be assigned to specific bond populations.

Bridging atomically precise materials design with scalable membrane manufacturing remains a central challenge in separation science. Two-dimensional covalent organic frameworks (2D COFs) offer tunable porosity and chemistry, but poor crystallinity and processability have limited their performance in membranes. Here we demonstrate that incorporating single-crystalline 2D COFs into anodic aluminum oxide supports and scalable mixed-matrix membrane enables exceptional separation performance, achieving ultrahigh permeance (MMM-SC-0.6: 8764 and 4531 L m–2 h–1 bar–1 for hexane and methanol, respectively) and near-quantitative rejection of nanoscale solutes. Reactive force field simulations through a multilayer COF model reveal the origins of rapid solvent transport and size selectivity through steric gating. These results establish highly crystalline COFs as practical, high-performance membrane materials and point to new routes for energy-efficient separations across chemical, environmental, and industrial technologies.