Search Results (4,160)

Junction spectroscopy techniques (JSTs) are powerful tools for investigating electrically active defects in semiconductors. Originally developed to study point-like defects in bulk semiconductors, JSTs have since been extended to increasingly complex systems, providing valuable insights into defect energetics and interactions. This review paper outlines the fundamental principles of JSTs and critically examines their application to emerging materials, such as perovskite solar cells and two-dimensional (2D) materials. By highlighting both the capabilities and limitations of JSTs in these non-classical systems, the review demonstrates their continued relevance and important role in advancing next-generation semiconductor materials and devices. Full article

MXene-based microwave absorbers have received extensive attention owing to their high electrical conductivity, abundant interfacial polarization sites, and tunable surface terminations. However, the structure–property relationship of MXene composites remains highly nonlinear, and the design of high-efficiency absorbers still relies heavily on trial-and-error experiments. Herein, multidimensional magnetic components, including zero-dimensional (0D) Fe₃O₄ nanoparticles, one-dimensional (1D) Fe₃O₄/Co₃O₄ nanowires, and two-dimensional (2D) Fe₃O₄-based heterostructures, were rationally integrated with Fe/MXene and Fe/Co/MXene nanosheets to engineer synergistic dielectric and magnetic losses. Comprehensive electromagnetic characterization and loss mechanism analysis reveal that the structural dimensionality strongly impacts impedance matching and attenuation capability. To further enable predictive and data-driven optimization, a machine learning framework was established to correlate the microstructure, component ratio, thickness, and electromagnetic parameters with the microwave absorption performance (e.g., minimum reflection loss (RL_min), effective absorption bandwidth (EAB)). The optimized multidimensional composite achieves an RL_min of −56.4 dB at 10.2 GHz with an EAB of 8.4 GHz (9.6–18.0 GHz) at a thin matching thickness of 1.8 mm. The machine learning model demonstrates excellent accuracy (R² = 0.947) and enables the inverse design of absorber geometries to target specific operational frequencies. This work provides a generalizable paradigm for the intelligent design of MXene-based microwave absorbers and opens up broader opportunities for the AI-accelerated discovery of advanced electromagnetic functional materials. Full article

Solid-state nanopore sequencing, a key third-generation sequencing technology, offers considerable potential for genomics and diagnostics due to its long read lengths, real-time detection, and amplification-free operation. The technology identifies DNA sequences by measuring characteristic changes in ionic current as single-stranded DNA translocates through a nanoscale pore. However, its practical development faces challenges including limited spatiotemporal resolution, pore clogging from nonspecific adsorption, and significant electrical noise. This review systematically examines strategies developed to address these limitations. We discuss the use of ultrathin two-dimensional materials such as graphene and molybdenum disulfide to improve spatial resolution, and methods to modulate DNA translocation through optimized solution conditions, pore geometry, surface charge engineering, and bio-solid hybrid pore designs. Furthermore, we detail noise suppression strategies targeting key sources like thermal noise, 1/f noise, and dielectric noise. These approaches encompass careful material selection, surface coatings, innovations in chip and amplifier design, and machine learning–based signal processing. The review also outlines surface functionalization techniques that reduce clogging and enhance analytical specificity. While challenges remain, continued convergence of materials science, nanofabrication, and data science is advancing solid-state nanopore technology toward reliable, high-precision sequencing platforms, promising to significantly impact personalized medicine and biological research. Full article

The crystal structures of the cobalt(II) metal–organic frameworks or coordination networks of [Co(pdb)(DMF)] and [Co₂(pdi)(DMF)₃]·2(DMF)·H₂O (H₂pdb = 3,3′-(phenazine-5,10-diyl)dibenzoic acid; H₄pdi = 5,5′-(phenazine-5,10-diyl)diisophthalic acid; DMF = N,N-dimethylformamide) were synthesized solvothermally from cobalt(II) nitrate and the free acid of the linker in DMF. Systematic solvothermal screening demonstrated strong metal- and counterion-dependent framework formation, as crystalline coordination polymers were obtained exclusively from cobalt(II) nitrate, whereas other metal salts and cobalt(II) chloride or sulfate produced no crystalline materials. In catena-[(N,N-dimethylformamide)-μ₄-3,3′-(phenazine-5,10-diyl)dibenzoate-cobalt(II)], [Co(pdb)(DMF)], the Co₂ units, acting as secondary building units, are coordinated by four carboxylate groups from four linkers in a paddle-wheel arrangement, giving a three-dimensional (3D) network with cds (or CdSO₄) topology, in which the wide openings are filled by two symmetry-related nets to form a threefold interpenetrated structure. In catena-[tris(N,N-dimethylformamide)-μ₈-5,5′-(phenazine-5,10-diyl)diisophthalate-dicobalt(II)] bis(N,N-dimethylformamide) hydrate, [Co₂(pdi)(DMF)₃]·2(DMF)·H₂O, there are two different Co atoms, of which only Co₂ is connected to each of the four carboxylate groups of the tetracarboxylate linker and, thus, is responsible for 3D network formation. The network topology in [Co₂(pdi)(DMF)₃] is pts (or platinum(II) sulfide) when taking the Co₂ atom as a tetrahedral node and the linker as a square-planar fourfold node; however, this arrangement is inverse to the common square-planar metal and tetrahedral linker nodes found in PtS and most pts topologies. Full article

Accumulation of tetracycline (TC) in aquatic environments poses a significant threat to human health and ecosystems, driving the need for efficient removal technologies. Two-dimensional metal–organic frameworks (2D MOFs) are promising adsorbents due to their tunable structures and abundant active sites. In this work, three 2D MOFs, M₃(HHTP)₂ (M = Cu, Ni, Co), were synthesized via a solvothermal method. Among them, Cu₃(HHTP)₂ exhibited superior TC adsorption with a maximum capacity of 302.84 mg/g. The adsorption process, best described by the Langmuir isotherm and pseudo-second-order kinetic models, indicates chemisorption. Mechanistic investigations reveal that the high-activity coordination sites formed by Cu²⁺ due to Jahn–Teller distortion enable strong coordination with TC. This is identified as the key factor governing the differential adsorption performance among the three MOFs. Simultaneously, the surface functional groups facilitate hydrogen bonding, and the advantageous pore structure of the material itself, together forming a synergistic adsorption. This work not only elucidates the microscopic mechanism behind the efficient adsorption of TC by Cu₃(HHTP)₂ but also, through comparative analysis of isostructural MOFs, confirms the decisive role of metal center electronic structure in modulating the adsorption behavior of 2D MOFs. The insights gained from this study may serve as a reference for the design of 2D high-performance adsorbents. Full article

This study presents a mechanics-based comparison of the buckling and ultimate strength behavior of stiffened plates reinforced with bulb-type and built-in angle stiffeners, with particular emphasis on the trade-off between structural performance and weight efficiency. Although these stiffener types are commonly treated as equivalent when designed to provide the same sectional moment of inertia, their nonlinear collapse behavior under realistic loading conditions has not been sufficiently quantified. To address this gap, a two-stage finite element framework is employed, consisting of linear eigenvalue buckling analysis to identify imperfection-sensitive modes, followed by geometrically and materially nonlinear imperfection analysis (GMNIA) to capture post-buckling behavior and ultimate strength. High-fidelity three-dimensional solid models incorporating classification-society-based material properties are used to simulate axially compressed stiffened plates representative of jack-up rig Living Quarter structures. The results demonstrate that, while both stiffener types exhibit comparable elastic buckling resistance, their nonlinear responses differ in terms of stiffness degradation, stress redistribution, and collapse localization. Importantly, the angle stiffener achieves an ultimate strength comparable to that of the elastically equivalent bulb stiffener while requiring less material, thereby exhibiting superior weight efficiency. These findings indicate that elastic equivalence alone is insufficient for optimal stiffener selection and highlight the necessity of nonlinear, imperfection-sensitive assessment in the design of lightweight and high-performance marine structures. Full article

Ti(C,N)-based cermet turning inserts with two distinct chip breaker groove structures were employed to investigate the influence of chip breaker geometry on cutting performance. Chip removal performance and wear resistance of the inserts were evaluated according to chip morphology. The results reveal that, compared with inserts with the V-type groove, those with the SF-type groove exhibit superior chip removal capability and enhanced flank wear resistance. Based on two key parameters of the equivalent groove width and initial chip curl radius, an oblique cutting model was proposed for turning inserts with three-dimensionally complex grooves. The model incorporates the coupled effects of chip breaker geometry, workpiece material properties, inserts material properties and cutting process parameters. By controlling chip morphology, the proposed model effectively realizes the improvement and rational optimization of cutting performance, providing a theoretical basis for the design and optimization of complex groove turning inserts. Full article

Two-dimensional (2D) materials are competitive in a diverse range of areas, spanning from electronic and optoelectronic devices to wearable devices, due to their unique physical and chemical characteristics, as well as remarkable flexibility. As a typical 2D material, lead iodide (PbI₂), featuring a high atomic number and tunable band gap, has been extensively studied in many applications of electroluminescent (EL) devices, photodetectors, and perovskite solar cells. However, high-performance PbI₂-based photodetectors remain a challenge. Herein, we present a high-performance flexible photodetector based on 2D layered PbI₂ nanoplates, which were synthesized via a straightforward air sublimation method. The PbI₂-based photodetector exhibits an excellent photoresponse and the highest responsivity peaks at 34 A/W at 405 nm, together with an ultrahigh transient switching on/off current ratio of 10⁷. Due to a low dark current (10⁻¹⁴ A), the device exhibits an extremely low noise level (<10⁻²⁶ A²Hz⁻¹) and acceptable detectivity (2 × 10¹⁰ Jones). Furthermore, remarkable mechanical flexibility was observed in the device on a PET substrate, preserving both its electrical conductance and photoresponse stability after 560 bending cycles. Finally, high-resolution imaging applications were implemented under a 100 Hz modulated light signal. This work highlights the superior optoelectrical properties of 2D PbI₂ growth by the in-air sublimation method and proves its promising future in flexible and wearable optoelectronic devices. Full article

Reconfigurable logic is crucial for future adaptive computing, but is challenging to realize with conventional complementary metal-oxide-semiconductor technology due to the limited field-effect characteristics of the fundamental silicon devices. Two-dimensional materials offer a promising platform, yet enhancing their functional versatility requires novel operational mechanisms. Here, we demonstrate a single WSe₂/h-BN/graphene heterojunction capable of dynamically switching between distinct logic functions—XNOR and IMP (implication gate or “IF-THEN” gate)—simply by modulating the drain-source voltage. At a low bias of 0.3 V, the carrier distribution is governed by capacitive coupling, realizing an XNOR gate. Increasing the bias to 3 V activates Fowler–Nordheim tunneling between the graphene floating gate and the drain, enabling IMP logic operation. The interplay and voltage-induced transition between these two physical mechanisms underpin the device’s multifunctional capability. This work introduces a novel operational strategy for two-dimensional material-based reconfigurable logic, providing a pathway toward compact, adaptive hardware for post-CMOS computing. Full article

Metal-free two-dimensional (2D) polymers built from open-shell π-conjugated units offer a promising platform for realizing correlation-driven magnetism without transition metal elements. Here, we present a systematic first-principles study of phenalenyl-based 2D polymers that elucidates how atomic-level chemical substitution controls magnetic order through the interplay of electronic correlation and sublattice symmetry. Combining density functional theory with an effective tight-binding and Hubbard model analysis, we show that atomic substitution with boron or nitrogen on phenalenyl building blocks acts as a sublattice-resolved tuning knob for both the ratio of on-site Coulomb interaction to inter-site hopping (U/t) and the relative on-site energies of the two sublattices. Sublattice-asymmetric substitution with boron or nitrogen breaks sublattice equivalence and drives the system from an antiferromagnetic Mott-insulating state into spin-polarized semiconducting phases with pronounced spin-dependent gaps. In contrast, uniform substitution on both sublattices preserves symmetry and yields nonmagnetic metallic states characterized by rigid band shifts rather than correlation-driven spin polarization. These results establish a unified microscopic framework in which electronic correlations and sublattice symmetry emerge as cooperative yet independently tunable parameters, providing general design principles for metal-free 2D π-conjugated materials with tailored magnetic and spintronic functionalities. Full article

This study investigates the application of the Pure Random Orthogonal Search (PROS) method, introduced in the literature in 2021, for approximating force and displacement measurement data obtained from rock specimen testing, using granite as a case study. The primary objective is to simplify the data approximation procedure and improve the accuracy of experimental data analysis by reducing the influence of subjective factors within a predefined protocol. The research focuses on determining the maximum value of the tangent modulus of elasticity during the pre-peak deformation stage of granite specimens under uniaxial compression. The study employs methods of mathematical modeling of rock mechanical behavior and experimental data analysis. To approximate the experimental data, a modified two-parameter S-curve equation is proposed. The optimal parameter values are determined using the PROS method, which reduces the problem to solving a two-dimensional objective function minimization task. The dimensionality of this optimization problem remains independent of the number of experimental data points, thereby enhancing computational efficiency. A systematic computational procedure is developed for the automated calculation of the approximating equation’s parameters and the determination of the maximum tangent modulus of elasticity. In the context of challenges associated with accurately measuring displacements using conventional testing machines, a numerical correction procedure is proposed and implemented to account for the compliance of the loading system. The results of the study are consistent with both the literature-reported experimental data and the data obtained in this work. The methodology and findings can be adapted for analyzing the properties of concrete as an artificial analog of natural rock materials. Full article

The blood–brain barrier (BBB), a core component of the neurovascular unit (NVU), meticulously regulates material exchange between the blood and brain parenchyma, serving as a critical barrier for maintaining the homeostasis of the central nervous system (CNS). Neuroinflammation, a pivotal response of the CNS to injury and disease, can disrupt NVU homeostasis when excessive or persistent, acting as a core pathogenic driver of various intractable neurological disorders. Chemokines, as key signaling molecules guiding the directional migration of immune cells, form the central hub mediating the dynamic regulation of neuroinflammation and the BBB. However, existing studies mostly focus on single disease systems or chemokine families, neglecting the bidirectional heterogeneity of different chemokine axes in BBB regulation and the common regulatory rules across diseases, while lacking systematic exploration of clinical translation challenges caused by the redundancy and spatiotemporal heterogeneity of the chemokine network. This review systematically clarifies the bidirectional regulatory effects of the core axes of the three major chemokine families (e.g., CCL2/CCR2, CXCL12/CXCR4, CX3CL1/CX3CR1) on the BBB. For the first time, we integrate a multi-dimensional regulatory model based on concentration, location, and time to analyze their molecular mechanisms and regulatory heterogeneity in promoting BBB disruption under pathological conditions versus mediating barrier repair and neuroprotection under specific spatiotemporal conditions. Combined with advancements in cutting-edge models such as microfluidic chips, we discuss the clinical translation progress of chemokine research, including potential biomarkers and targeted therapeutic strategies, and propose precise breakthrough paths for the two core challenges of network redundancy and spatiotemporal heterogeneity. Finally, we construct a complete research framework for chemokine-mediated regulation of NVU homeostasis, providing novel insights and directions for restoring BBB function and treating intractable neurological diseases. Full article

Industrial robots are widely used in critical tasks such as assembly, welding, and material handling as core components of modern manufacturing systems. For the reliable operation of these systems, early and accurate detection of execution failures is crucial. In this study, a comprehensive comparison of machine learning and deep learning methods is conducted for the classification of robot execution failures using data acquired from force–torque sensors. Three different feature engineering approaches are proposed. The first is a Baseline approach that includes 90 raw time-series features. The second is the Domain-6 approach, which consists of 6 basic statistical features per sensor (36 in total). The third is the Domain-12 approach, which comprises 12 comprehensive statistical features per sensor (72 in total). The domain features include the mean, standard deviation, minimum, maximum, range, slope, median, skewness, kurtosis, RMS, energy, and IQR. In total, ten classification algorithms are evaluated, including eight machine learning methods and two deep learning models: Support Vector Machines (SVM), Random Forest (RF), k-Nearest Neighbors (KNN), Artificial Neural Network (ANN), Naive Bayes (NB), Decision Trees (DT), eXtreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LightGBM-LGBM), as well as a One-Dimensional Convolutional Neural Network (CNN-1D) and Long Short-Term Memory (LSTM). For traditional machine learning algorithms, 5 × 5 nested cross-validation is used, whereas for deep learning models, 5-fold cross-validation with a 20% validation split is employed. To ensure statistical reliability, all experiments are repeated over 30 independent runs. The experimental results demonstrate that feature engineering has a decisive impact on classification performance. In addition, regardless of the feature set, the highest accuracy (93.85% ± 0.90) is achieved by the Naive Bayes classifier using the Baseline features. The Domain-12 feature set provides consistent improvements across many algorithms, with substantial performance gains. The results are reported using accuracy, precision, recall, and F1-score metrics and are supported by confusion matrices. Finally, permutation feature importance analysis indicates that the skewness features of the Fx and Fy sensors are the most critical variables for failure detection. Overall, these findings show that time-domain statistical features offer an effective approach for robot failure classification. Full article

This study investigates the gasification of pistachio shells for the co-production of biochar and renewable energy, integrating process simulation, energy recovery, and techno-economic–environmental assessment. The investigation has been carried out by experimental tests in a 30 kg/h downdraft gasification–cogeneration system and process simulation. The zero-dimensional simulation model, validated against first-hand experimental data, was used to evaluate two operational scenarios differing in biochar yield (10% and 17%) and energy yield. The integration of the gasification–CHP system with a representative pistachio-processing facility (500 t yr⁻¹ shell availability) demonstrated annual useful energy outputs ranging from 574 to 900 MWh yr⁻¹ (as the sum of heat and electricity). The techno-economic analysis yielded operating profits of 96,720–117,637 € yr⁻¹, return on investment (ROI) between 15.5% yr⁻¹ and 18.85% yr⁻¹, and payback periods of 6.45 and 5.3 years for the high- and low-char scenarios, respectively. The environmental assessment revealed total CO₂-equivalent savings of 241–279 t yr⁻¹, with biochar sequestration contributing up to 41% of avoided emissions. Overall, the results confirm that higher carbon conversion to syngas enhances energy, environmental and economic performance, while higher biochar yields favour fixing carbon in the soil, according to the assumed scenarios’ conditions. The proposed framework demonstrates a scalable, sustainable solution for coupling pistachio-shell gasification with industrial energy and a material valorization pathway. Full article

Earth-based materials are increasingly considered as low-carbon alternatives for sustainable building construction. However, the high variability of natural soils and the complex behaviour of their clay fraction remain major barriers to the standardisation of characterisation and formulation methods. This study proposes a methodological and experimental framework based on the fine fraction (<400 µm) of soils to predict the fresh-state and hardened-state performance of earthen construction materials. Two natural soils from southwestern France with contrasted mineralogical compositions were investigated using rheological studies, compaction, linear shrinkage, and unconfined compressive strength (UCS) tests. The results show that the fine fraction plays a dominant role in governing material behaviour: smectite-rich soils reach higher dry densities (up to ≈2.10 g·cm⁻³) and compressive strengths (up to ≈6 MPa) but exhibit greater shrinkage sensitivity, whereas kaolinite–illite-rich soils display reduced shrinkage and improved dimensional stability. By demonstrating the predictive capacity of fine-fraction-based indicators for mechanical performance and dimensional stability, this work contributes to the development of simplified, reproducible, and environmentally relevant methodologies for the design of low-carbon earthen building materials using locally sourced soils. Full article