Search Results (980)

The pyrolysis of polypropylene (PP) microplastics offers a potential route to convert plastic waste into fuel-range hydrocarbon mixtures and chemical feedstocks. However, the elementary radical pathways underlying the formation of medium-chain hydrocarbon fragments remain insufficiently resolved. In this study, a representative isotactic PP oligomer model (C₄₅H₉₂) was evaluated using a comparative density functional theory (DFT) framework. The main mechanistic analysis was based on M06-2X, ωB97X-D, and M11 calculations combined with the def2-TZVP basis set, whereas LANL2DZ was retained only as a lower-cost comparative level during reaction-pathway exploration. Thermochemical profiles were evaluated over a temperature range of 298–923 K. Three selected pathways involving mid-chain homolytic cleavage, intramolecular hydrogen transfer (backbiting), radical rearrangement, and β-scission were examined. Within the selected reaction set, Route 1 exhibited a comparatively more favorable thermochemical profile than Routes 2 and 3 and provided a mechanistically plausible sequence toward medium-chain hydrocarbon fragments. The −TΔS contribution strongly influenced the calculated Gibbs free-energy profiles because fragmentation increases the number of molecular species under the ideal-gas thermochemical approximation. Accordingly, the ΔG values were interpreted comparatively and were not treated as direct evidence of spontaneous fragmentation under condensed-phase pyrolysis conditions or as quantitative predictions of experimental product selectivity. Differences among the evaluated functionals further indicate that the relative description of radical intermediates and transition-state regions is method-dependent. These results provide a molecular-level framework for future studies integrating quantum-chemical calculations, microkinetic modeling, and experimental product characterization. Full article

Post-Quantum Cryptography (PQC) migration to National Institute of Standards and Technology (NIST) Federal Information Processing Standards (FIPS) 203, 204, and 205 under the National Security Agency (NSA) Commercial National Security Algorithm Suite (CNSA) 2.0 is a multi-year, multi-domain transformation across cloud, enterprise, embedded, operational technology (OT), tactical, and national-security systems. Anthropic’s Claude Mythos Preview (April 2026) introduces artificial intelligence (AI)-accelerated cybersecurity capabilities that intersect this migration directly, performing autonomous reasoning against previously unknown vulnerabilities in production software—a qualitative departure from signature-based and static and dynamic application security testing (SAST/DAST) tooling. Drawing on federal guidance from NIST, NSA, the Office of Management and Budget (OMB), and the Cybersecurity and Infrastructure Security Agency (CISA), and on independent analyses from the Centre for Emerging Technology and Security (CETaS) and the UK AI Security Institute, we present a lifecycle and architecture analysis of how Mythos-class models alter PQC migration timelines, risk surfaces, lifecycle dependencies, and architectural constraints. Modeling Mythos as both accelerator and destabilizer, we derive an analytic projection of a compressed two-to-four-year migration window for highest-exposure systems, against traditional baselines of five-to-ten years for small organizations and twelve-to-fifteen-plus years for large enterprises. The compression collapses human-labor bottlenecks in discovery, planning, and code modification, not cryptography itself. We propose a lifecycle-aligned migration model, an updated cost model, and governance requirements for frontier-model access. The binding constraint shifts domain-conditionally: defender capacity at adversary tempo governs software-analytical phases, while non-compressible external cadence governs embedded and regulated domains. Full article

Intelligent packet routing in sixth-generation (6G) heterogeneous wireless networks must contend with stochastic link failures, heterogeneous delay profiles, and the severe memory constraints of edge nodes. We propose a quantum deep Q-network (Q-DQN) that replaces the multi-layer perceptron in a standard DQN agent with a six-qubit variational quantum circuit (VQC) employing ring-topology entanglement and angle embedding. The total trainable parameter count follows the closed-form expression $\left|\mathsf{\varphi }\right|=12L+7n$, growing at only seven parameters per additional network node. On a 10-node heterogeneous topology with stochastic link failures, Q-DQN achieves an average end-to-end delay of $54.29\pm 1.72$ ms with only 106 parameters, a 49.6× reduction relative to the MLP-based DQN baseline (5258 parameters, $52.89\pm 2.67$ ms). A three-seed scalability evaluation across $n\in \{6,8,10,12\}$ nodes shows that under a limited 200-episode training budget DQN converges more consistently, while Q-DQN matches DQN performance under full 500-episode training at a fraction of the parameter cost. Ablation experiments confirm that local-topology entanglement substantially outperforms full-connection alternatives. These results indicate that VQC-based routing agents can match classical counterparts at a fraction of the parameter cost, providing a path toward ultra-lightweight intelligent routing in 6G edge deployments. Full article

Bionic sensors are emerging as powerful analytical platforms driving the development of next-generation detection technologies, particularly for small molecule sensing in complex environmental and biological systems. However, accurate and selective detection of small molecules remains fundamentally challenging due to their low molecular weight, limited structural specificity, and strong interference from complex matrices. This review provides a comprehensive overview of recent advances in bionic sensor technologies, focusing on how the integration of synthetic biology, nanomaterials, and artificial intelligence (AI) addresses these limitations. Key biorecognition elements, including enzymes, antibodies, aptamers, and molecularly imprinted polymers, are examined for their suitability in small molecule sensing applications. Advances in nanomaterials such as graphene, carbon nanotubes, quantum dots, and MXenes are discussed in relation to signal transduction enhancement, sensitivity improvement, and device miniaturization. In parallel, the roles of AI and machine learning in signal denoising, adaptive calibration, and molecular fingerprinting for complex datasets are highlighted. Applications in wearable and implantable biosensors, environmental monitoring, and food safety are analyzed, emphasizing real-time detection of metabolites, pollutants, and toxins. Key challenges associated with AI-driven systems, including scalability, cost, data reliability, and ethical concerns, are also discussed. Emerging trends such as hybrid sensing platforms, self-powered biosensors, and secure data integration frameworks are presented as future directions. This review aims to provide a problem-driven perspective on how next-generation bionic sensors can overcome current limitations and enable robust small molecule detection in real-world applications. Full article

Foreign-exchange decisions rest on hierarchically organized evidence whose latent structure is inadequately captured by Euclidean representations. Reinforcement-learning agents trained on flat embeddings inherit stability guarantees that do not transfer to the manifold supporting the latent state. We address both limitations through a hybrid architecture in which a schema-constrained structured chain-of-thought is embedded into a Poincaré ball, transported to a qubit register via angle encoding, and processed by an L-layer hardware-efficient variational ansatz on a state-vector backend. The circuit exposes two read-outs to the policy, namely, a scalar Pauli-Z observable and a projected quantum kernel inducing a fidelity-based similarity over magnet-price attractors, the latter identified via kernel-weighted recurrence density and finite-time Lyapunov statistics. The Lipschitz constraint on the action-value function is lifted from the hyperbolic geodesic distance to a joint metric on ${\mathbb{B}}_{\kappa }^n\times \mathbb{P}\left(\mathcal{H}\right)$. A stability theorem yields an explicit bound depending on the read-out operator norm, on the depth–width product of the ansatz, and on the curvature–Hilbert balance. The pipeline is evaluated on nine major FX crosses over a 2015–2025 out-of-sample window, with rolling-origin walk-forward retraining and broker-published transaction costs. The system attains $2.55\%$ pair-averaged non-compounded monthly P&L and $8.83\%$ maximum drawdown, with Sharpe $1.78$, Calmar $3.43$, and Probabilistic Sharpe Ratio exceeding $0.95$ on every cross. The gain remains significant under a deflated-Sharpe-ratio test with $N_{\mathrm{trials}}=42$ correction. Block-wise ablations exhibit strictly monotone degradation: removing the projected kernel costs $-4.15$ p.p. on annualized P&L, the joint Lipschitz penalty $-6.42$ p.p., the attractor module $-7.64$ p.p., and the hyperbolic embedding $-8.40$ p.p. The quantum block thereby instantiates a structurally non-classical, geometry-aware regularizer identifiable through ablation rather than asymptotically advantageous. Full article

The strategy of enhancing biocatalytic activity through the modification of natural cells with nanomaterials has overcome the intrinsic catalytic bottlenecks of bacteria, making significant contributions to energy production and pollution treatment. However, chemically engineered biocatalyst systems remain in their early stages of development. Herein, we report a simple and straightforward strategy for constructing an efficient biocatalyst by incorporating carbon quantum dots (CDs) into Escherichia coli (E. coli) to enhance the oxygen reduction reaction (ORR) at the cathode of microbial fuel cells (MFCs). The introduction of CDs significantly accelerates extracellular electron transfer and metabolic activity, markedly increases intracellular adenosine triphosphate (ATP) levels, and promotes substrate utilization. Furthermore, the engineered E. coli exhibits enhanced surface adhesion and increased electronegativity. Electrochemical measurements demonstrate superior ORR activity, delivering a maximum current density of 3.1 mA·cm⁻² and an onset potential of 0.67 V, outperforming many previously reported biocatalysts. When applied in an MFC system, the modified biocatalyst achieves a maximum power density of 325 μW·cm⁻², placing it among the highest-performing systems reported to date. This work provides a facile and cost-effective approach for improving MFC performance and offers a promising design strategy for next-generation biohybrid catalysts. Full article

We develop a systematic framework for incorporating perturbative correction terms into classical finite difference schemes for Allen–Cahn type stochastic partial differential equations. Three distinct correction approaches are introduced, conceptually motivated by perturbative quantum field theory, quantum coherent state evolution, and WKB (Wentzel–Kramers–Brillouin) barrier penetration theory. These quantum-inspired perturbative method (QIPM) corrections function as classical perturbations executing entirely on conventional hardware; quantum-mechanical formalism serves only as a design principle for constructing specific functional forms of correction terms. The primary novelty of this work lies in (i) a generic convergence-preservation theorem establishing sufficient conditions on correction magnitude for any perturbative correction to maintain the base scheme’s accuracy order, and (ii) a systematic translation methodology from quantum-mechanical analogies to explicit, implementable finite difference corrections with rigorous parameter bounds. Through convergence analysis, we demonstrate that appropriately parametrized corrections preserve the accuracy of the underlying numerical scheme, provided the solution possesses sufficient regularity and the parabolic scaling constraint $\Delta t=\mathcal{O}\left(h^2\right)$ holds. Numerical experiments on a spatially discretized domain over a finite time horizon using spatially correlated noise reveal that the anharmonic oscillator correction achieves exceptional accuracy with modest computational overhead, while the amplitude encoding correction provides intermediate accuracy with negligible timing cost. The tunneling-inspired correction exhibits higher error for smooth initial conditions, indicating strong problem-dependence. While these methods enhance accuracy in specific scenarios, genuine speedups on classical hardware are not achieved. The primary value lies in establishing systematic methodologies for perturbative correction design and developing theoretical foundations for future hybrid computational approaches. Full article

Accurately solving high-dimensional partial differential equations (PDEs) remains a central challenge in computational mathematics. Traditional numerical methods, while effective in low-dimensional settings or on coarse grids, often struggle to deliver the precision required in practical applications. Recent machine learning-based approaches offer flexibility but frequently fall short in terms of accuracy and reliability, particularly in industrial contexts. In this work, we explore a quantum-inspired method based on quantized tensor trains (QTT), enabling efficient and accurate solutions to PDEs in a variety of challenging scenarios. Through several representative examples, we show that the QTT approach can achieve logarithmic scaling in memory and computational cost for linear PDEs when the relevant QTT ranks remain moderate. We also develop QTT space-time formulations that treat time as an additional dimension, allowing the full temporal evolution to be represented and solved globally rather than through sequential time stepping. For the nonlinear Burgers equation, we study both time-stepping and a frozen-coefficient space-time Picard scheme in QTT form, and report empirical convergence behavior on smooth one-dimensional viscous test problems. Additionally, we present a proof-of-concept data-driven workflow within the quantum-inspired framework, in which sampled source data are interpolated into QTT form and then incorporated directly into the structured PDE solver. Full article

The increasing global demand for renewable energy has intensified the search for high-efficiency and cost-effective solar cell technologies. Quantum dot-sensitized solar cells (QDSSCs) have emerged as promising candidates due to their tunable optoelectronic properties and enhanced light absorption. In this study, SnS quantum dots were synthesized from dithiocarbamate complexes using different ligands, namely m-toluidine (SnS1), aniline (SnS2), and p-toluidine (SnS3), to investigate the influence of precursor chemistry on material properties and device performance. Structural analysis confirmed the formation of an orthorhombic phase for all samples, while morphological studies revealed well-dispersed nanocrystals for SnS1 (5.93 nm), increased aggregation for SnS2 (8.57 nm), and partially fused domains with an intermediate size for SnS3 (6.67 nm). Optical measurements showed bandgap energies of 2.8, 2.2, and 2.7 eV for SnS1, SnS2, and SnS3, respectively, with SnS3 exhibiting reduced charge-recombination behaviour. Photovoltaic devices fabricated using these materials yielded power conversion efficiencies of 3.40, 2.03, and 7.63% for SnS1, SnS2, and SnS3, respectively, with no significant improvement observed for bifacial configurations. The superior performance of SnS3 is attributed to an optimal balance between light absorption, morphology, and charge transport properties, highlighting the critical role of precursor ligand selection in tuning quantum dot characteristics for improved QDSSC performance. Full article

Post-quantum cryptographic (PQC) algorithms are being developed to guard against quantum-computing attacks, but their behavior in constrained Internet of Things (IoT) environments remains an important topic of discussion. In this work, we study the impact of deploying PQC protocols in IoT networks using the Open Quantum Safe (liboqs) framework. In particular, key encapsulation and digital signature schemes are evaluated in terms of their computational performance, communication costs, and energy consumption. Our results indicate that although PQC operations can be completed in microseconds using general-purpose processors, substantially larger key and ciphertext sizes introduce significant communication overhead. When mapped to common IoT protocols such as Bluetooth Low Energy (BLE), IEEE 802.15.4 (Zigbee), and LoRa, these larger payloads must be divided into multiple packets. In low-payload LoRa networks, for example, ML-KEM handshakes can require up to 62 packets. This level of fragmentation increases latency and energy consumption, thus potentially affecting reliability. Furthermore, when packet delivery probabilities approaching 99% are achieved, handshake success rates can drop to values approaching 50%. These results suggest that communication metrics, rather than computational performance, pose key challenges to PQC deployment in constrained IoT settings. Full article

The rapid advancement of quantum computing poses a fundamental threat to classical public-key cryptographic systems, necessitating the transition to post-quantum cryptography (PQC). While significant progress has been made in the standardization of quantum-resistant algorithms, their practical deployment in heterogeneous environments—particularly resource-constrained Internet of Things (IoT) devices—remains a critical challenge. This study presents a comprehensive experimental evaluation of four NIST-standardized PQC algorithms: CRYSTALS-Kyber (ML-KEM), CRYSTALS-Dilithium (ML-DSA), FALCON, and SPHINCS+. The scope of these findings is bounded by an empirical analysis conducted across two specific testing platforms, a high-performance x86-64 workstation (AMD Ryzen 7 5700U) and a resource-constrained embedded microcontroller (ESP32-WROOM), utilizing dedicated software environments implemented in Native C, Go, and Python. The evaluation isolates key performance indicators, including computational latency, memory consumption, communication overhead, and temporal determinism, based on benchmarking over 1000 iterations. Within this experimental setup, results demonstrate clear trade-offs between target security categories, execution performance, and structural memory limits. Lattice-based schemes such as Kyber and Falcon exhibit optimal efficiency and scalability on the tested embedded platform, while the specific memory limits of the ESP32 platform introduce architectural stability constraints for higher-tier Dilithium variants. In contrast, SPHINCS+ provides structural robustness at the cost of higher computational hashing latency within these evaluation environments. The findings highlight the critical role of hardware-specific constraints and language runtime design choices in enabling practical PQC deployment, providing context-specific insights supporting the secure migration of IoT infrastructures toward quantum-resilient systems. Full article

The increasing pace of Internet of Things (IoT) and Industrial Internet of Things (IIoT) applications has exacerbated the security challenges in resource-constrained environments, where traditional cryptographic protocols incur prohibitively high computational and energy costs. These constraints are also worsened by the advent of quantum computing, which poses a long-term security risk to popular crypto-key cryptographic-based efforts. To overcome these difficulties, this paper proposes an Energy-Efficient Cryptographic Protocol Framework (EECPF) that provides mutual optimization between energy consumption, security level, and communication latency to achieve sustainable IoT security. The presented framework proposes an adaptive encryption selection mechanism that dynamically chooses cryptographic algorithms depending on device capabilities, network conditions, and threat levels derived from intrusion detection outputs. EECPF combines privacy-preserving federated learning for distributed intrusion detection with collaborative threat intelligence sharing, eliminating centralized data sharing. In addition, lattice-based post-quantum cryptography primitives are added and combined with lightweight blockchain-enforced identity management to ensure long-term authentication resilience. The models on which the framework is based are mathematically based, modeling the consumption of energy, the robustness of security, and latency, providing principled multi-objective optimization under resource constraints. The publicly available Edge-IIoTset dataset was subjected to extensive experimental assessment under realistic IIoT and IoT attack scenarios. Experiments show that EECPF can reach an intrusion detection rate of 94.7%, while reducing energy consumption by 47.3% and latency by 23.8% compared with other commonly used lightweight cryptographic methods. These were continually noticed across different heterogeneous devices and deployment environments. In general, EECPF offers an energy-aware, quantum-resilient, and scalable security solution that can be used for next-generation IoT systems, such as smart healthcare, industrial automation, and smart city infrastructures. Full article

This review evaluates the role of advanced materials in optimizing the efficiency, sustainability, and market integration of solar photovoltaic (PV) technologies. Our work bridges insights from both mature (crystalline silicon (c-Si)) and novel perovskites (PSs), organic photovoltaics (OPVs), and quantum dot solar cell (QDSC) materials, thereby providing a unified view of the present and the future of PV research. We highlight the key breakthroughs for the different material classes, describing their unique features, record performance, and contribution to lowering the cost of solar energy. In particular, while some progress has been made, we recognize that challenges such as the stability of the device under varying environmental conditions, the environmental impact of the materials, and the scalability of the manufacturing processes are still there. In conclusion, we give an overview of the research topics that can pave the way for the future. We support the formation of hybrid structures, the finding of lead-free alternatives, multi-junction architectures, and integrated solutions that not only help to overcome the current limitations but also facilitate the global energy transition. Full article

Quantum machine learning on near-term noisy quantum devices has generated substantial theoretical interest, but rigorous empirical comparisons under realistic noise on practically relevant data remain scarce. This paper compares two paradigmatic quantum learning models, a Quantum Support Vector Machine (QSVM) built on the ZZFeatureMap quantum kernel and a Variational Quantum Classifier (VQC) with an EfficientSU2/RealAmplitudes ansatz, against tuned classical baselines (SVM with four kernels, Random Forest, XGBoost, LightGBM and CatBoost) on the ULB Credit Card Fraud dataset (284,807 transactions, 0.17% fraud). All models share an identical 4-qubit PCA-reduced feature space, evaluated on the full unbalanced test fold over 15 fits (3 folds × 5 seeds) and reported as mean ± standard deviation with bootstrap confidence intervals, AUPRC as the primary metric. Noise robustness is assessed under depolarizing noise $p\in \{0,0.001,0.01,0.05\}$, with ranking preservation measured directly through Spearman $\rho$ and Kendall $\tau$ between the noisy and noiseless decision scores rather than read off AUPRC, alongside the per-paradigm computational cost. At four qubits the classical baselines lead (AUPRC 0.60 to 0.74, CatBoost best), above the VQC (0.494) and the QSVM (0.240); the controlled QSVM-versus-RBF–SVM comparison puts the cost of the quantum kernel at about 0.45 AUPRC. Under noise the QSVM keeps its score ranking ($\rho =0.998$ at $p=0.001$, 0.906 at $p=0.01$) and an operational decision threshold (recall 0.87 to 0.89, stable calibration), while the VQC AUPRC peaks non-monotonically at $p=0.01$ (0.494 rising to 0.654, then easing to 0.569 at $p=0.05$) even as its ranking decays monotonically ($\rho$ from 0.72 to near zero), so average precision on its own misrepresents how noise affects it. The quantum models do not surpass the tuned classical reference at four qubits; the contribution is methodological: under noise, AUPRC has to be read together with a genuine rank statistic, because the two can move in opposite directions. Full article

A Quantum Key Distribution (QKD)-assisted Physical Layer Security (PLS) scheme for Multiple-Input Multiple-Output (MIMO) wireless links is proposed and numerically evaluated. The framework utilizes high-rate quantum keys to generate unitary precoding matrices for channel estimation preamble encryption, alongside a constellation-based encryption methodology for the data payload. Integration of the QKD is facilitated by a practical Key Management System (KMS) that orchestrates key synchronization and ensures seamless interoperability with the QKD infrastructure. By securing both the preamble and payload portions of the transmission frame, the proposed scheme prevents unauthorized entities from acquiring critical knowledge of transceiver functionalities. Furthermore, the framework leverages high-entropy QKD-derived keys to reseed a pseudo-random number generator (PRNG), providing a symmetric-key encryption layer that enhances data confidentiality. Numerical evaluation results obtained within a simulated residential wireless environment demonstrate that the proposed architecture yields enhanced security at the cost of a minor degradation in reception performance, driven by a small noise amplification penalty and a marginal elevation in the peak-to-average power ratio (PAPR). Full article