Browse Articles

Mechanically interlocking cyclic star polymers quenches solvent-dependent properties

We simulate star polymers with cyclic arms formed via click reactions and study the effects of solvent quality on the resulting mechanical interlocking complexity and radius of gyration. We find that polymers with sufficiently long arms cyclized in a poor solvent present a higher degree of interlocking among arms with respect to those cyclized in a good solvent. Furthermore, when a polymer cyclized in a poor solvent is moved to a good solvent, its radius of gyration is smaller than that of star polymers cyclized in a good solvent, indicating that cyclization can quench a solvent-dependent property. Importantly, we show that the number of arms—or functionality, f—affects the degree of interlocking in poor solvents. Due to an asymmetric collapse transition, if f is sufficiently small, all arms phase separate to one side of the star’s central core; they can hence all interact with each other, increasing interlocking. When f is large enough, the entire surface of the core is covered by the arms, hindering interactions between faraway arms and decreasing interlocking. We identify a critical grafting density for the transition via a geometric argument, and we set a criterion for the formation of a single mechanically interlocked blob, that is, the arm’s length must be larger than half of the core’s circumference.

A comprehensive study on the line profiles and Stark widths of ionic transitions from laser-produced aluminum plasma

We present a systematic spectroscopic investigation of laser-produced aluminum plasma to address inconsistencies in Stark broadening parameters and establish a self-consistent reference data sets for electron density diagnostics. Optical line emissions of Al II and Al III in the visible wavelength range were recorded from plasmas having different electron densities and temperatures, however, with the same experimental configuration, only by varying the background pressure, spatial position, and delay time. The Stark width parameter of Al III lines, which shows consistency across different earlier studies, is used for standardizing the Al II transition from the highest energy level, which is abundant in the emission spectra. This reference spectrum is then used to estimate the Stark parameters of other Al II transitions to obtain a self-consistent database for Al II transitions. This approach significantly reduces the uncertainty in the estimated plasma electron density using Stark parameters of multiple emission lines. We also report the spatial and temporal evolution of plasma density and Stark shift as well as asymmetry in spectral lines. This work addresses the uncertainty in Stark parameters of Al II transitions in the visible range through a unified approach in estimating these parameters simultaneously.

Machine learning-enhanced modeling approach for optimally predicting household level food insecurity in Ethiopia during COVID-19

Abstract Food insecurity remains a critical global challenge, with low-income countries such as Ethiopia bearing a disproportionate burden. In settings where frequent data collection is limited, developing predictive models provides a cost-effective means of anticipating risks to enhance rapid life-saving action, supporting timely evidence-based interventions. This study develops predictive models, applying Machine Learning (ML) approaches, capable of accurately forecasting household-level food insecurity. This study used data from the Ethiopia-High Frequency Phone Survey, collected by World Bank. The performances of Machine Learning models and classical logistic regression model, in predicting households’ food insecurity, were compared. Predictive models were trained and validated (internally and temporally) to evaluate model generalizability over time. ML models significantly outperformed the traditional logistic regression model in predicting household food insecurity. Based on the Brier score, the ML models demonstrated higher predictive accuracy and better calibration than the classical model. Regularization through optimized hyperparameters improved model stability and feature selection. While ridge, lasso, and elastic-net regressions produced similar coefficient directions, they differed in the number of selected predictors—the lasso model identified minimum key variables with comparable predictive accuracy. Temporal validation confirmed that the ML models maintained strong predictive performance, demonstrating their reusability and generalizability across time. The lasso regression model demonstrated strong predictive capability by selecting a manageable set of relevant features, resulting in reduced model complexity and improved interpretability. Integrating such ML models into food security monitoring systems can help policymakers design timely and data-driven interventions, enabling proactive responses in resource-constrained environment.

Modeling stochastic chemical kinetics on quantum computers

The Chemical Master Equation (CME) provides a highly accurate yet extremely resource-intensive representation of stochastic chemical reaction networks and their kinetics due to the exponential scaling of its possible states with the number of reacting species. In this work, we investigate how quantum computing can be employed to model stochastic chemical kinetics as described by the CME using the Schlögl model of a trimolecular reaction network as an illustrative example. We analyze the mono- and bistable regimes of the Schlögl model, identifying the bistable regime as more suitable for quantum computation due to the availability of a Hermitian operator form that preserves eigenvalues and eigenvectors. Employing the Variational Quantum Deflation (VQD) algorithm, we compute the smallest-magnitude eigenvalues, λ0, and λ1. We use VQD and a combination of Quantum Phase Estimation (QPE) and Variational Quantum Singular Value Decomposition (VQSVD) to estimate the zeromode (non-equilibrium steady state) of the bistable case. Our results from noiseless quantum simulations (VQD) and quantum hardware (QPE + VQSVD) agree within a few percent with the classically computed eigenvalues and zeromodes of up to 4-qubit operators. We show that achieving an exact solution requires at least 5 qubits, which is within reach of near-term quantum computers.

Electronic transport and thermoelectric performance in quasiperiodic Fibonacci bilayer graphene superlattices

We investigate the electronic transport and thermoelectric performance of Fibonacci bilayer graphene superlattices (FBGSLs) and compare them systematically with their periodic counterparts. The study, conducted for high-order Fibonacci generations, utilizes a four-band effective Dirac Hamiltonian, the Sturm–Liouville formalism, and the numerically stable hybrid matrix method within the Landauer–Büttiker formalism. Our results demonstrate that while periodic arrangements provide robust transport through broad minibands, the aperiodic order of the Fibonacci sequence induces a fragmentation of the transmittance and the emergence of critical states with multifractal characteristics. This fragmentation effectively activates additional energy regions with high thermoelectric response. However, we find that periodic bilayer graphene superlattices (PBGSLs), characterized by a well-defined boxcar-shaped transmission band in the hole region, achieve a superior simultaneous optimization of both conversion efficiency and maximum power output. We find that the Seebeck coefficient reaches values up to ±0.6 mV/K, the power factor and the figure of merit exhibits sharp peaks about 4 pW/K2 and 15, respectively. While the FBGSLs induced fragmentation enables high-precision energy filtering in specific regions, the PBGSLs remains the optimal configuration for maximizing the efficiency-power trade-off, allowing for conversion efficiencies that saturate near the theoretical limit (≈0.5ηC) for finite-power output quantum heat engines operating within the linear-response regime. These findings suggest a dual strategy for device design: utilizing aperiodicity for multi-band spectral selectivity and periodicity for peak power-efficiency optimization.

Oral squamous cell carcinoma is associated with altered salivary microbiome structure and reduced community evenness

All-electron quasiparticle self-consistent <i>GW</i> for molecules and periodic systems within the numerical atomic orbital framework

We report an all-electron implementation of the quasiparticle self-consistent GW (QSGW) method for molecular and periodic systems within the framework of numerical atomic orbitals (NAOs), as implemented in the LibRPA software package. Our implementation is based on the space-time formalism, combined with the localized resolution-of-identity approximation to treat two-electron quantities. We found that analytical continuation of the self-energy matrix, in combination with the “Mode B” QSGW scheme, can yield stable self-consistent quasiparticle energy spectra. Systematic benchmark calculations on molecules and crystalline solids (including typical semiconductors and wide-gap insulators) demonstrate that our NAO-based QSGW scheme yields molecular ionization potentials and quasiparticle bandgaps for periodic solids that are consistent with reference results from established implementations. Our work opens the way for large-scale QSGW calculations, taking advantage of the NAO-based low-scaling algorithm previously developed for the G0W0 method.

<i>Ab initio</i> description of de Haas–van Alphen oscillation using relativistic magnetic-Bloch-states

The ab initio description of the de Haas–van Alphen (dHvA) oscillation has been recognized as one of long-standing goals in material science. Here, we demonstrate that the dHvA oscillation can be described by the ab initio method for calculating relativistic magnetic-Bloch-states of materials immersed in a uniform magnetic field, the nonperturbative magnetic-field-containing relativistic tight-binding approximation (nonperturbative MFRTB) method. Furthermore, we provide two kinds of useful tables for calculating relativistic magnetic-Bloch-states. One is the relativistic version of the Slater–Koster table, and the other is a table of nonperturbative magnetic hopping integrals for all combinations related to d-orbitals. Since these tables can be commonly used not only for the nonperturbative MFRTB calculations but also for investigating magnetic properties caused by relativistic magnetic-Bloch-states, the publication of these two tables opens the way for material design that utilizes magnetic fields as parameters for controlling physical properties.

Sex differences in post-exercise hypotension after a recreational beach tennis session in adults with controlled hypertension: a randomized crossover trial

Superhard porous carbon crystals derived from nanoporous ice frameworks

Porous carbon crystals are of great interest due to their high surface area and tunable porosity, which endow them with superior properties for a range of applications. The discovery of novel porous carbon architectures, therefore, holds both scientific and practical value. In this study, we propose twenty porous carbon crystals designed by analogy with nanoporous ice frameworks following the “ice-carbon” strategy. Among these, thirteen are reported for the first time, highlighting the effectiveness of this approach for predicting new carbon allotropes. The dynamic and mechanical stabilities of the proposed structures are confirmed through phonon spectrum analysis and elastic constant calculations, respectively. Importantly, these allotropes are energetically more favorable than experimentally synthesized T-carbon, suggesting their potential for experimental realization. Notably, fifteen of the twenty structures are classified as superhard materials, with Vickers hardness exceeding 40 GPa. Among them, four structures (PC-T-5.6.8, PC-O-4.6.8, PC-O-5.8, and PC-O-5.6.8 with the hardness values of 66.77, 65.63, 73.70, and 68.98 GPa, respectively) surpass the hardness of cubic boron nitride. Furthermore, all twenty porous carbons are identified as semiconductors with bandgaps ranging from 1.54 to 3.66 eV. Combining superhard character with high porosity, these superhard porous carbon materials show strong potential for applications in aerospace materials, battery components, catalysis, and photodetectors.

Frequency-sweep force volume AFM-IR: Decoupling infrared mapping from mechanical properties

Infrared nano-spectroscopy by atomic force microscopy-infrared (AFM-IR) couples an atomic force microscope (AFM) to tunable infrared (IR) laser radiation to perform infrared signature mapping of complex samples at a nanometric spatial resolution. Recently, the new frequency-sweep force volume AFM-IR operating mode was introduced, offering a way to measure the full frequency response of the cantilever-sample system during IR mapping. Such operating mode enables to integrate the frequency-dependent IR signal over resonance modes, thus incorporating into the IR response the resonance line shape and hence the magnitude of the mechanical damping of the system. Unlike conventional resonance-tracking methods (as implemented in AFM-IR contact, tapping, and peak force tapping), the frequency-sweep force volume AFM-IR mode performs IR mapping without requiring active resonance adjustment. This makes it particularly suitable for mechanically heterogeneous samples with substantial frequency shifts and low signal-to-noise ratios. In this work, we present a systematic AFM-IR study on standard polymer samples to showcase the IR mapping capabilities of the frequency-sweep force volume mode compared to resonance-enhanced contact and resonance-enhanced force volume modes. This study highlights that frequency spectra are a crucial tool to evaluate the suitability of a specific resonance mode to perform IR mapping and thus to interpret AFM-IR data. Integrating the frequency response of the system furthermore allows to improve the IR contrast on heterogeneous regions.

Using an RNA binding protein to detect mRNA on lipid nanoparticles

Reconstruction of spin structures from topological charge distributions via generative neural network systems

Localized topological defects inherently possess a multiscale character. While their microstructure configuration depends on the specific physical system, their topological features and mutual interactions can be described on the macroscale in terms of a particle representation. However, determining the physical properties associated with a given defect pattern often requires knowledge of the underlying microscopic structure. In this study, we extend a Wasserstein generative adversarial neural network by incorporating physical constraints and Fourier-space information to generate microscopic spin configurations consistent with prescribed macroscopic patterns and thermodynamic parameters. Using the two-dimensional XY model as a test case, where vortex–antivortex pairs act as long-range interacting defects, we show that the model generates spin configurations that accurately reproduce magnetization, susceptibility, helicity modulus, and spin–spin correlations over a wide range of temperatures below the Kosterlitz–Thouless transition. At the same time, deviations in the specific heat reveal limitations in reproducing higher-order energy fluctuations. A complementary analysis based on topological data analysis uncovers subtle differences in global spin-correlation structures at near-critical temperatures that are not apparent from conventional correlation functions alone. These results demonstrate both the promise and current limitations of generative approaches for multiscale studies of defect-dominated spin systems and, at the same time, highlight topological methods as valuable tools for characterizing critical behavior.

Rapid and continuous reduction of silicon nanoparticles’ size and crystallinity through the interaction with multistage atmospheric-pressure microwave plasma system

Silicon nanomaterials have significant applications in energy, semiconductor, and life-science fields, where they are in strong demand, yet still lack robust, scalable, and high-quality large-scale production methods. This study demonstrates the size reduction of micrometer-sized silicon particles using a three-stage atmospheric-pressure microwave-plasma system, with a processing time of approximately 100 ms. The plasma temperature field was measured using an optical emission spectroscopy method. SEM (scanning electron microscopy) and TEM (transmission electron microscopy) were utilized to examine the surface morphology of the products, and the particle size distribution of the prepared products was statistically analyzed with a minimum mean diameter of 20.71 nm and a standard deviation of 11.51 nm. XRD (x-ray diffraction) and Raman spectroscopy confirmed the continuous reduction of silicon nanoparticles’ size and crystallinity. The experimental results indicate that multistage atmospheric microwave-plasma treatment can reduce the size of silicon nanoparticles in an ultra-fast, continuous, one-step process, offering promising prospects and developmental potential for the economical, high-throughput production of quantum-dot-scale silicon nanoparticles. The simultaneous reduction of particle size and crystallinity demonstrated here is of particular relevance to silicon-based lithium-ion battery anodes, where amorphous nanoparticles below 30 nm exhibit markedly superior cycling stability, and to silicon quantum dot photonic applications, where sub-30-nm diameters activate quantum confinement effects that shift the photoluminescence into the visible range.

Prevalence and factors associated with open defecation practices among household population in Tanzania

Steady-state analysis of mean first-passage times with stochastic switching

One can find the mean-first passage time (MFPT) of a particle diffusing in a closed domain by solving the adjoint Smoluchowski equation with appropriate boundary conditions. An alternative approach to determining the MFPT that exploits the fact that the MFPT is given by the inverse of the Kramers flux-over-population ratio was proposed by Reimann, Schmid, and Hanggi (RSH). In this approach to find the MFPT, one has to determine the steady-state number of particles in the domain maintained by a constant flux injected at the particle starting point and divide this number by the injected flux. Here, we consider the MFPT of a particle diffusing in a cylindrical cavity to a circular spot of arbitrary radius located on the cavity base and generalize the RSH approach to the case where the spot radius and particle diffusivity stochastically switch between two values. This generalization allows one to find the MFPT as a function of the particle diffusivities and the spot radii in the two states, the cavity length and radius, the particle starting distance from the cavity base, and the switching rates. Comparison of our theoretical predictions with the MFPT obtained from Brownian dynamics simulations shows excellent agreement between the two.

Limited diffusion of silicon in GaN: A DFT study supported by experimental evidence

Silicon (Si) is the primary donor dopant in gallium nitride (GaN), introduced through epitaxial growth or ion implantation. However, precise control over Si diffusion remains a critical challenge for high-performance device applications. This study investigates Si diffusion mechanisms in bulk GaN using density functional theory (DFT) calculations, supported by ion implantation (I/I) and ultrahigh-pressure annealing (UHPA) experiments. Vacancy-mediated diffusion pathways were analyzed using the SIESTA code, with minimum energy paths (MEPs) and migration barriers determined via the nudged elastic band (NEB) method. The results indicate that Si diffusion barriers vary with the crystallographic direction, with the lowest barrier of 3.2 eV along [112¯0] and the highest barrier of 9.9 eV along [11¯00], rendering diffusion in this direction highly improbable. Phonon calculations confirm that temperature-induced reductions in effective diffusion barriers are minimal. Experimental validation using SIMS analysis on Si-implanted GaN samples subjected to UHPA (1450 °C, 1 GPa) confirms negligible Si diffusion under these extreme conditions. These findings resolve inconsistencies in prior reports and establish that Si in GaN remains highly stable, ensuring reliable doping profiles for advanced electronic and optoelectronic applications.

Early fusion of laser and acoustic features for human orientation detection in non-line-of-sight environments

Multimer embedding for molecular crystals utilizing up to tetramer interactions

Molecular crystals possess a highly complex crystallographic landscape, which in many cases results in the experimental observation of multiple crystal structures for the same compound. Accurate results can often be obtained for such systems by employing periodic density functional theory using hybrid functionals; however, this is not always computationally feasible. One possibility to circumvent these expensive periodic calculations is the utilization of multimer embedding methods. Therein, the fully periodic crystal is described at a lower level of theory, and subsequently monomer energies, dimer interaction energies, etc., are corrected via high-level calculations. In this paper, we further extend such a multimer embedding approach by one multimer order for all investigated properties, allowing us to compute lattice energies up to the tetramer embedding level, and atomic forces, the stress tensor, and harmonic phonons up to the trimer level. We test the significance of including these higher-order multimers by embedding PBE0+MBD multimers into periodic PBE+MBD calculations utilizing the X23 benchmark set of molecular crystals and comparing the results to explicit periodic PBE0+MBD calculations. We show that tetramer interactions systematically improve the lattice energy approximation and explore multiple possibilities for multimer selection. Furthermore, we confirm that trimer interactions are crucial for the description of the stress tensor, yielding cell volumes, on average, within 0.3% of those of PBE0+MBD. Subsequently, this also results in an improvement in the description of vibrational properties, giving on average Gamma-point frequencies within 1.3 cm−1 and vibrational free energies within 0.3 kJ/mol of the PBE0+MBD results.

Lattice-matched nitride-oxide HEMTs with superior electronic and thermal performance

β-Ga2O3 has gained significant interest for applications in high-power devices because of its large energy bandgap of Eg = 4.4–4.8 eV, a high breakdown field of ∼7–8 MV/cm, and availability of melt-grown large area bulk substrates. (AlxGa1−x)2O3/Ga2O3-based heterostructure devices have also been realized for high-power and high-frequency applications. The performance of these devices is, however, limited by the low conduction band offsets resulting from difficulties in growing high-Al composition β-(AlxGa1−x)2O3. Additionally, low thermal conductivity of β-Ga2O3 can result in severe self-heating effects and potential early failure of devices, restricting its application in high temperature and high-power electronics. In this work, an in-depth analysis of lattice mismatch in the heterojunctions between (0001) oriented III-nitrides (AlN, GaN, BN, and InN) and (2¯01) oriented β-phase III-sesquioxides (Al2O3, Ga2O3, and In2O3) and their alloys is conducted. An AlN/β-(In0.21Ga0.79)2O3 heterojunction is found to have good lattice match with high conduction band offset along with a polarization charge induced 2-dimensional electron gas (2DEG) at the interface with density ∼23× the theoretically maximum 2DEG carrier density achievable in β-(Al0.2Ga0.8)2O3/Ga2O3 interface. Through detailed thermal simulations under different cooling conditions, we show significantly lower self-heating effect (RTH reduction of ∼41%) in AlN/β-(In0.21Ga0.79)2O3 high electron mobility transistors (HEMTs) compared to β-(Al0.2Ga0.8)2O3/Ga2O3-based HEMTs. Additionally, a physics-based compact model is used to calculate practically achievable current density in the presence of self-heating effects in these structures. For similar temperature rise, AlN/β-(In0.21Ga0.79)2O3 HEMTs are found to be ∼5× superior in terms of current carrying and power handling capability compared to β-(Al0.2Ga0.8)2O3/Ga2O3 HEMTs.