Evaluating the security of a device against side-channel attacks is a difficult task. One prominent strategy for this purpose is to characterize the distribution of the rank of the correct key among the different key hypotheses produced by a maximum likelihood attack, depending on the number of measured traces. In practice, evaluators can estimate some statistics of the rank that are used as security indicators—e.g., the arithmetic and geometric mean rank, the median rank, the α-marginal guesswork, or the success rate of level L. Yet, a direct estimation becomes time-consuming as security levels increase.In this work, we provide new bounds on these figures of merit in terms of the mutual information between the secret and its side-channel leakages. These bounds provide theoretical insights on the evolution of the figures of merit in terms of noise level, computational complexity (how many keys are evaluated) and data complexity (how many side-channel traces are used for the attack). To the best of our knowledge, these bounds are the first to formally characterize security guarantees that depend on the computational power of the adversary, based on a measure of their informational leakages. It follows that our results enable fast shortcut formulas for the certification laboratories, potentially enabling them to speed up the security evaluation process. We demonstrate the tightness of our bounds on both synthetic traces (in a controlled environment) and real-world traces from two popular datasets (Aisylab/AES_HD and SMAesH).

All-dielectric metasurfaces based on quasi-bound states in the continuum (quasi-BICs) have emerged as a powerful platform for nanophotonic sensing, as they support high-Q resonances and strong near-field enhancements. Herein, we propose and numerically investigate an asymmetric bow-tie metasurface composed of two silicon semi-cylinders with unequal radii and a central bar to achieve a quasi-BIC resonance with a Q-factor of 11,000. The transition mechanism of the BIC modes in the asymmetric bow-tie metasurface is analyzed. Additionally, the spectral features of the asymmetric bow-tie metasurface as a function of the refractive index and temperature of the local environment are also investigated. The proposed structure exhibits a refractive index sensitivity of 454 nm/RIU and a temperature sensitivity of 134 pm/°C. Furthermore, a high figure of merit (FOM) of 3159 RIU−1 is achieved, and the nearly 100% modulation depth maintained across three distinct resonance dips. Our study suggests that the proposed asymmetric bow-tie metasurface offers a promising approach for the development of high-sensitivity biosensing platforms.

Oriented synergistic assembly of one-dimensional Te nanowires/SWCNTs for high-performance thermoelectric aerogels.

Cryotherapy and radiofrequency (RF) treatments modulate tissue temperature to induce therapeutic effects; however, improper application can result in thermal injury. Traditional temperature-based monitoring methods rely on multiple thermal sensors whose accuracy strongly depends on their number and spatial positioning, often failing to detect early tissue crystallization. This study introduces a fractional order bioimpedance modelling framework for the early detection of tissue freezing during cryogenic and thermal medical treatments, with the feasibility and effectiveness of this approach having been reported in our prior publications. While bioimpedance spectroscopy itself is a well-est. The corresponablished technique in biomedical engineering, its novel application to predict and identify premature freezing events provides a new pathway for safe and efficient energy-based therapies. Fractional-order models derived from the Cole family accurately reproduce the complex electrical behavior of biological tissues using fewer parameters than classical integer-order models, thus reducing both hardware requirements and computational cost. Experimental impedance data from human abdominal, gluteal, and femoral regions were modelled to extract fractional parameters that serve as sensitive indicators of phase-transition onset. The results demonstrate that the proposed approach enables real-time identification of freezing-induced electrical transitions, offering a physiologically grounded alternative to conventional temperature-based monitoring. Furthermore, the fractional order bioimpedance method exhibits high reproducibility and selectivity, and its analytical figures of merit, including the limits of detection and quantification, support its use for reliable real-time tissue monitoring and early injury detection. Overall, the proposed fractional order bioimpedance framework enhances both safety and control precision in cryogenic and thermal medical applications.

Resonant-type piezoelectric vibration energy harvesters (pVEHs) can achieve high energy conversion efficiency and output power density by using resonance phenomena with external vibrations at constant frequencies. However, this approach is less effective for the random vibrations commonly encountered in real-world environments, thus limiting its practical applications. This study employs impulsive forces and a two-degree-of-freedom system with a dynamic magnifier (DM) to enhance the output power of a nonresonant microelectromechanical systems-based pVEH (MEMS-pVEH). Lead-free BiFeO3 (BFO) was selected as the piezoelectric thin film due to its considerable figure of merit in energy conversion applications. We investigated the electromechanical properties of the BFO-based MEMS-pVEH using DM under various impulsive force durations. The results revealed that incorporating a DM increased the output energy of the MEMS-pVEH from 0.17 nJ/G2 to 3.97 nJ/G2 (where G represents gravitational acceleration) for a 23.8-fold enhancement. The findings demonstrate that the output energy of conventional MEMS-pVEH structures using lead-free piezoelectric BFO film can be significantly amplified with the integration of a simple DM.

Graphene functionalized with catalytic transition metals offers high-performance chemiresistive gas sensing by coupling graphene's exceptional electronic transport with the metal's catalytic activity; yet the atomistic relationships connecting synthesis parameters, morphological outcomes, and sensor gas-surface reaction kinetics remain elusive. We developed an equivariant machine-learning interatomic potential with DFT accuracy to enable high-fidelity molecular dynamics (MD) simulations, from metal nanostructure growth on graphene to device-level sensing kinetics. Demonstrating our approach, we specifically investigate Pt-functionalized graphene for H2 detection. MD simulations validated by TEM show that Pt deposition begins with dispersed nuclei coalescing into polycrystalline nanoclusters, while both MD and Raman spectroscopy reveal predominantly noncovalent metal-graphene interactions that induce moderate local strain and doping, while preserving graphene's structural integrity. MD simulations confirm H2 dissociative chemisorption and recombinative desorption primarily on Pt nanoclusters, with negligible spillover or chemical interaction with pristine graphene. However, H adsorption on Pt attenuates the Pt-graphene interfacial binding, providing an indirect electronic pathway for gas sensing. Adsorption/desorption kinetics reveal that an intermediate loading of metal nanostructures minimizes the detection limit; lower loadings facilitate faster response and recovery kinetics and enhance signal transduction, whereas higher loadings increase interfacial binding and graphene doping. The developed machine-learned MD framework accurately models metallic nanostructure growth on graphene, elucidates the gas-sensing mechanism, and correlates figures of merit─detection limit, sensitivity, and response/recovery times─extracted from gas-surface kinetics with metal nanostructure morphology, establishing a multiscale predictive pipeline from synthesis conditions to gas-sensor kinetics.

The Finite State Model Predictive Control (FSMPC) of variable speed drives is the subject of many works in the recent literature. Many variants of FSMPC exist, each aiming at an aspect such as the complexity of the cost function, switching frequency, current quality, etc. In the case of multiphase drives, two popular variants are the monovector and multivector techniques. Despite past efforts to compare different techniques, the field must still reach a consensus regarding the relative merits of each one. This paper presents a new method to compare two families of FSMPC. The method is based on a reduced set of figures of merit using the current modulation index as the variable. The comparison is made for the equal usage of the power converter in terms of commutations. The results point to better values for the figures of merit for the monovector that, in addition, portrays more flexibility and better DC link usage.

Helicenes are chiral π-conjugated molecules whose properties strongly depend on their lengths. Systematic studies of these compounds have been limited by synthetic challenges. Here we report a concise two-step strategy (defined as the helicene-forming sequence from aminohelicene precursors) to access a homologous series of tetraaza[7]-[15]helicenes. Optical spectra converge beyond [11]H, defining a conjugation ceiling, while chiroptical responses amplify sharply, yielding |glum| up to 0.028. Fluorescence quantum yields show a nonmonotonic dependence, with [7]H and [15]H maintaining both high ΦF (0.39 and 0.36) and large |glum|, resulting in outstanding, albeit semi-quantitative, CPL performance, with figures of merit reaching 0.010 and CPL brightness values of approximately 490. TD-DFT calculations attribute this amplification to the delayed alignment of electric and magnetic transition dipoles, while 1H NMR shifts of inner protons provide an independent probe of structural reorganization within the helical cavity. Additionally, experiment and theory have consistently identified [11]H as the critical helicene length at which the framework undergoes a qualitative transition. Notably, the [15]helicene constitutes the longest helicene ever resolved into its enantiomers, underscoring the synthetic power of this modular approach. Importantly, our synthetic route is effective for constructing higher-order helicenes, offering a generalizable platform for length-controlled, heteroatom-containing helicenes. These findings establish long tetraazahelicenes as a rare platform where through-bond conjugation and through-space orbital coupling act cooperatively to govern photophysical and chiroptical properties.

Objective.The aim of this study is to investigate the relation between two figures of merit for the low contrast resolution of computed tomography (CT) imaging systems, with the perspective of its use for acceptance and constancy testing.Approach.We use simulated data as well as 29 CT image datasets of the MITA body phantom CCT189 obtained using a previously published protocol, including CT devices from five different manufacturers and various image reconstruction methods. From these data, the detectability indexd' is determined using the channelised Hotelling observer (CHO), which requires hundreds of images per setting. We compared' to the minimum detectable contrast (MDC), a statistically defined measure of low contrast detectability, that can be determined using only few images per setting.Main results.For the CHO with circular symmetric DDOG (dense difference of Gaussians) channels,d' is proportional to the inverse of the product of MDC and the diameter of the object to be detected. The proportionality factor depends strongly on the texture of the noise.Significance.The findings provide the basis for the development of an acceptance and constancy test for CT low contrast resolution, making use ofd'CHO and MDC.

Abstract This work presents an inverse design framework for the active regions of InAs-based interband cascade lasers (ICLs), combining LightGBM and differential evolution (DE) algorithm to address inefficiencies and local optima in high-dimensional structural optimization. A dataset of 74,773 structure-performance mappings was built using an in-house wavefunction recognition algorithm based on the 8-band k · p model. Among six regression models, LightGBM achieved the best performance, predicting key metrics-transition energy ( E g ), squared matrix element (| M | 2 ), and injector energy-level difference (Δ E inj )-with RMSEs as low as 0.0010, 0.0078, and 0.0010. SHAP analysis confirmed the model's interpretability and its ability to capture nonlinear structural effects.The LightGBM-DE integration enabled efficient inverse optimization over the 3-13 μm range of the target operation wavelength, producing structures with higher Figure of Merit (FoM) than literature benchmarks. Notably, 90.7% of the optimized samples showed absolute errors within 0.025 when validated against the 8-band k · p model, confirming high reliability. Novel structures for 9.00 μm and 10.00 μm were also successfully designed, demonstrating strong generalization. This framework provides a fast, accurate, and interpretable solution for the intelligent design of ICLs and other semiconductor heterostructures.

The production of green hydrogen via photoelectrochemical water splitting has the potential to play a vital part in the decarbonization of our energy economy. To commercialize this technology, the stability of the current photoelectrode materials needs to be improved. Therefore, it is essential to understand the processes causing degradation and define suitable figures of merit. This work investigates the degradation mechanisms of oxynitride electrodes focusing on TiO2 necked LaTiO2N particle-based photoanodes. Their degradation behaviour was assessed by chronoamperometries at 1.23 V vs. RHE in basic electrolyte. We identified two current decay processes based on a semi-empirical correlation between the measured chronoamperometries and a sum of two exponential decay terms. The time constant of the second exponential function is proposed as an alternative figure of merit for quantifying the stability of oxynitride based photoanodes. The applicability of this figure of merit to a wider range of oxynitride-based photoanodes and measurement conditions is demonstrated by evaluating previously reported chronoamperometries. By in depth analysis before and after chronoamperometry using STEM-EDX/EELS, HREM, ICP-MS, and XPS, we find experimental evidence that the performance decrease of the LaTiO2N photoanodes is caused by a combination of surface oxidation and cocatalyst dissolution.

Design and implementation of high-performance thermoelectric (TE) devices pose significant challenges from both theoretical and experimental perspectives. Utilizing experimentally synthesized eight-carbon-wide armchair graphene nanoribbons with built-in periodic divacancies (DV8-aGNR), we address these challenges with three effective strategies: periodic pores, nonplanarity, and vertical junctions, all with the goal of minimizing phonon thermal conductivity and achieving a high figure of merit (ZT). Through first-principles calculations, we firstly investigate the TE performance of DV8-aGNR, which reveals that the periodic divacancies and nonplanar characteristics can effectively reduce phonon thermal conductivity while enhancing electrical conductance. A maximum ZT value of 0.64 at room temperature and 0.87 at 500 K in DV8-aGNR is 337% and 414% times that of the armchair graphene nanoribbon with the same width. Then the proposed van der Waals junction further restricts phonon transmission and exhibits improved TE properties, with ZT values rising to 1.70 and 1.97 at 300 and 500 K, respectively. The enhancement of ZT observed in DV8-aGNR and its vertical junction underscores the potential of our strategies for developing carbon-based TE devices with high performance.

The electronic, magnetic, optical, and thermoelectric properties of the artificially engineered (Mn, Mo) and (Mn, Ni) codoped ZnS systems have been investigated using density functional theory (DFT) within the Wien2K Package. Calculations were carried out using both the generalized gradient approximation (GGA) and the modified Becke–Johnson (mBJ) potential to ensure reliable electronic and magnetic descriptions. The double codoping of ZnS with (Mn, Mo) and (Mn, Ni) leads to the formation of an artificial half-metallic material, where both parallel and antiparallel spin configurations converge toward a ferromagnetic solution. However, the most stable phase corresponds to a ferrimagnetic configuration. The half-metallic character originates from strong p–d hybridization between transition-metal orbitals, which plays a crucial role in determining the material's multifunctional properties. The optical response exhibits a noticeable redshift in the absorption edge and distinct plasmonic structures, demonstrating the potential of such half-metallic systems for optoelectronic and photonic applications. Furthermore, thermoelectric calculations reveal that (Mn, Mo) codoping induces a p-type Seebeck coefficient with a figure of merit (ZT) ≈ of 1.2. In contrast, (Mn, Ni) codoping exhibits n-type behaviour with an enhanced ZT ≈ 1.6. These results highlight that (Zn1−2xMnxAxS) (A = Mo, Ni) represents a promising artificial half-metallic material with significant potential for multifunctional spintronic, optoelectronic, and thermoelectric applications.

Tyrosinemia is an inherited metabolic disorder that occurs due to the disruption in the breakdown of the amino acid ‘tyrosine’. Elevated levels of tyrosine cause liver failure, and neurological damage in newborns. Hence, rapid and accurate detection of tyrosine is essential for the timely management and diagnosis of this disorder. In this context, a non-enzymatic electrochemical sensor has been fabricated using Graphene Quantum Dots (GQDs) functionalized screen-printed carbon electrode for the detection of tyrosine. The different functional groups present on the surface provides efficient binding and recognition of tyrosine. The electrochemical signatures of the fabricated electrodes exhibited a sensitivity of 0.03 µA µM−1, detection limit of 0.102 µM, linear range of 5–60 µM, and quantification limit of 0.3094 µM. The selective nature of the sensor was confirmed in the presence of possible interfering amino acid species and the promising figure of merits may be tested to detect tyrosine in clinical samples.

We have developed a magnetic holographic memory using transparent bismuth-substituted rare-earth iron garnet as a next-generation optical memory. To realize this, a magnetic garnet with a large Faraday rotation angle and a moderately small extinction coefficient is required. In this study, we investigated the effect of Al or Ga substitution for the iron site of bismuth-substituted yttrium iron garnet (Bi/YIG) films on their magneto-optical properties. The Faraday rotation angle decreased with the amount of substitution, x, increase, for both Al- and Ga-substituted Bi/YIG, and a reversal of sign of rotation angle was only observed for Ga-substituted Bi/YIG, indicating a compensation composition. In the Al-substituted sample, due to small squareness, the residual Faraday rotation angle at zero magnetic field, |θR,res|, gradually decreased above x = 0.5, whereas in the Ga-substituted sample, the squareness ratio increased with increasing substitution up to x = 2.0, and thus showed a peak at x = 1.5. The Curie temperature and extinction coefficient were reduced with increasing substitution amount. As a result of a decrease in extinction coefficient, k, the high figure of merit, (|θR,res|/2πk) · λ was obtained around x = 1.5~1.9 for Ga and x = 2.1 for Al, while it was smaller than that of Bi/RIG we usually used.

GGA and GGA+U investigation of half-metallic ferromagnetism in cubic EuVO3 perovskite.

Van der Waals (vdW) layered materials have gained significant attention owing to their distinctive structure and unique properties. The weak interlayer bonding in vdW layered materials enables guest atom intercalation, allowing precise tuning of their physical and chemical properties. In this work, a ternary compound, NixIn2Se3 (x = 0-0.3), with Ni randomly occupying the interlayers of In2Se3, is synthesized via an intercalation route driven by electron injection. The intercalated Ni atoms act as "anchor points" within the interlayer of In2Se3, which effectively suppresses the phase transition of In2Se3 at elevated temperatures. Furthermore, the disordered Ni intercalation significantly enhanced the electrical conductivity of In2Se3 through electron injection, while reducing the thermal conductivity due to the interlayer phonon scattering, leading to an improved thermoelectric performance. For instance, the thermoelectric figure of merit (ZT) of Ni0.3In2Se3 increased by 86% (in-plane) and 222% (out-of-plane) compared to In2Se3 at 500°C. These findings not only provide an effective strategy to enhance the performance of layered thermoelectric materials but also demonstrate the potential of intercalation chemistry for expanding the application scope of van der Waals (vdW) layered materials.

Thermomechanical infrared (IR) detectors have emerged as promising alternatives to traditional photon and thermoelectric sensors, offering broadband sensitivity and low noise without the need for cryogenic cooling. Despite recent advances, the field still lacks a unified framework to guide the design of these nanomechanical systems. This work addresses that gap by providing a comprehensive design guide for IR thermal detectors based on silicon nitride drumheads and trampolines. Leveraging a validated analytical model, we systematically explore how geometry, tensile stress, and optical properties influence key performance metrics such as thermal time constant, noise-equivalent power, and specific detectivity. The analysis encompasses both bare silicon nitride and structures with broadband absorber layers, revealing how different parameter regimes affect the trade-off between sensitivity and response speed. Rather than focusing on a single device architecture, this study maps out a broad design space, enabling performance prediction and optimization for a variety of application requirements. As such, it serves not only as a reference for benchmarking existing devices but also as a practical tool for engineering next-generation IR sensors that can operate close to the fundamental detection limit. This work is intended as a foundational resource for researchers and designers aiming to tailor IR detectors to specific use cases.

Titanium nitride (TiN) thin films demonstrate high electrical conductivity and thermal stability up to 400 °C in ambient conditions, with stability extending to 600-800 °C under inert or vacuum environments. Unlike many metals and transition metal nitrides, TiN combines high carrier mobility with moderate carrier concentration, making it ideal for thermal management and power-efficient applications in nanoelectronics and energy harvesting. This study systematically investigates the thermoelectric and electronic transport properties of TiN films grown by plasma-enhanced atomic layer deposition (PEALD), comparing them to those produced using traditional thermal atomic layer deposition (thermal ALD). These properties are studied as a function of growth temperature and the number of growth cycles. In particular, TiN films deposited by PEALD at 400 °C for 2000 ALD cycles exhibited a remarkable power factor of 512 µW m-1 K-2 at room temperature compared to a power factor of 4.95 µW m-1 K-2 measured for thermal ALD films fabricated under the same deposition conditions. Additionally, thermal conductivity was also measured for thicker TiN films (86 nm), yielding values of 26.96 W m-1 K-1 for PEALD and 7.01 W m-1 K-1 for thermal ALD, marking the first such report for ALD-grown TiN. These values offer an upper estimate of the thermal behavior in thinner films. Based on these measured properties, the thermoelectric figure of merit (zT) at room temperature was calculated to be 0.0056 for PEALD TiN films which is significantly higher than the value of 0.0002 obtained for thermal ALD TiN films. Our findings provide critical insights into transport properties of TiN, offering guidance for the development of conductive nanolayers in thermoelectric, nanoelectronic, and on-chip cooling applications, where precise control over thermal and electronic behavior is vital, thereby expanding the relevance of ALD TiN in high-performance applications.

Figure of merit for evaluating the thin-film encapsulation's performance via mechanical, barrier and optical properties