Journal of Physics D: Applied Physics

Twisted bilayer (TB) MoS₂ has attracted great interest due to its application in twistronics. A modification of the conventional growth method is usually required to overcome the energy barrier between layers to grow TB MoS₂ with different angle. Hence direct one-step synthesis of MoS₂ with a large area, clean surface, and a wide twisted angle range is still a challenge. In this work, we show the direct growth of high-quality TB and trilayer of MoS₂ by a countercurrent physical vapor deposition method. We investigated the evolution of optical properties of twisted MoS₂ at the range from 0° to 60°. Raman and photoluminescence results show a strong relationship between interlayer coupling and the twisted angle of bilayer MoS₂. Moreover, interlayer exciton was observed in TB MoS₂ for a large twisted angle range below 253 K. In comparison, no interlayer exciton related emission was observed for transferred bilayer MoS₂, indicating that bottom–up growth of twisted MoS₂ presents better interlayer quality. Our results demonstrate a simple approach to produce twisted angle MoS₂ with high quality for twist-angle based optical and electronic properties investigations.

The coupling of catalysts and atmospheric-pressure plasma has the potential to improve the efficiency of certain catalytic reactions. Understanding the changes that the catalyst surface undergoes during exposure to plasma is key to improving plasma–catalytic performance. In this work, long term exposure of Pt–Al₂O₃ powder catalyst to an Ar/N₂/O₂ non-equilibrium atmospheric-pressure plasma-jet was investigated. Products produced by the interaction were analyzed downstream with Fourier-transform infrared spectroscopy while surface species were analyzed operandi with diffuse reflectance infrared Fourier transform spectroscopy. During exposure, the catalyst temperature was ramped cyclically between 100 °C and 350 °C to understand how substrate temperature affects the plasma–catalyst interaction. Long-lasting changes were revealed to take place on the catalyst surface during plasma exposure. At low temperatures, Pt–O and Pt–NO accumulate on the surface which react at elevated temperatures to form NO₂. NO₂ initially appears to spill on to the Al₂O₃ support as nitrites and nitrates instead of desorbing. Stable surface conditions are only achieved after prolonged plasma exposure, when nitrate sites on the Al₂O₃ support are filled. By changing the catalyst temperature at various rates, the impact of total plasma species flux to the surface was analyzed. It was found that decreasing the heating rate increased the hysteresis in the pattern of NO₂ formation during thermal cycling. The variation with temperature demonstrates that plasma exposure results in a buildup of surface NO_x and oxygen species which react or desorb at high temperatures. The observed changes are discussed from the generic viewpoint that a non-equilibrium plasma interacting with a catalyst at low temperature introduces metastable steady-state surface conditions. Upon heating above a threshold temperature, the introduced surface modifications can change either due to thermal effects, or, for a plasma environment, by additional interaction with the incident plasma species flux. The surface/material changes take place in a highly predictable fashion and after sufficient time above the threshold temperature reach a steady-state condition that is different from the transient behavior that is observed during initial heating. During cooling the plasma-surface interaction exhibits a different behavior than during heating, and this results in hysteresis of diverse observables. The metastability/hysteresis description appears quite generic and analogous to hysteresis behavior seen for different systems. It is expected to be useful for understanding the consequences of plasma–catalyst surface interactions for various systems.

Benefitting from its wide bandgap and robust ionic bonding nature, β-Ga₂O₃ is a critical material in extreme radiation environments. To investigate its radiation-resistant properties and microstructure evolution, molecular dynamics simulation is employed to systematically study the impact of different primary knock-on atom (PKA) energies (1.5, 3.0, 5.0 and 7.0 keV) and different temperatures (173, 300 and 800 K) on radiation-induced defects along [010] direction in bulk β-Ga₂O₃ crystals. The result shows that the Frenkel pairs (FPs) yield increases linearly with PKA energy. The threshold displacement energy of Ga and O were calculated. Although the increase in temperature slightly improves the defect recombination rate, it also leads to more defects during the radiation cascade collisions. This occurs because the elevated temperature influences the movement of displaced atoms, creating more branch-like small sub-cascades. These branches cause greater local energy deposition, forming damage regions and resulting in more defects after irradiation. Additionally, when the energy exceeds 1.5 keV, sub-cascade clusters begin to split, indicating an energy-temperature coupling mechanism. This study is crucial for enhancing the displacement damage resistance of β-Ga₂O₃-based devices and provides a foundation for subsequent testing and analytical results of β-Ga₂O₃ and related materials.

We report the role of the bandgap energy in Cd$_{1-x}$Mn_xTe thin films in the photoinduced crystallization of tellurium under prolonged exposure to visible light. Raman spectroscopy was used to quantify the evolution of the trigonal phase of tellurium over time. Our results reveal that when the energy of incident photons exceeds the bandgap energy, the photocrystallization process seems to be controlled by diffusion and consistent with the growth of one-dimensional tellurium forming crystalline structures. Conversely, when the excitation energy is lower than the bandgap energy, the photoinduced effect is completely suppressed. These findings provide valuable insights into how bandgap engineering can be utilized to control material properties in thin film systems, potentially advancing the development of novel semiconductor devices.

Combining first-principles calculations and nonadiabatic (NA) molecular dynamics simulations, this study explores the electronic structures, optical properties and photoexcited charge carrier dynamics in GeC/MoSSe with two stacking configurations. Electrostatic potential analysis demonstrates stacking-dependent interfacial electric fields in the heterostructures. Notably, the type-II GeC/SMoSe heterolayer exhibits an electric field that promotes ultrafast charge separation with electron and hole transfer time of 68 fs and 40 fs, respectively, via multiple intermediate electronic states serving as efficient transfer channels. Moreover, electron–hole recombination time in GeC/SMoSe is prolonged to 94.2 ns, nearly seven times as long as that of GeC/SeMoS, which originates from reduced NA coupling and enhanced decoherence. These findings reveal the critical role of Janus asymmetry-induced interfacial electric fields in tailoring charge carrier dynamics in van der Waals heterostructures for promising applications in optoelectronics.

Graphene is an atomically thin material composed of a single layer of carbon atoms arranged in a hexagonal lattice, which exhibits unique electrical, thermal, and mechanical properties. The intentional introduction of foreign atoms into the structure of graphene by doping is a powerful approach for modifying these properties, making graphene suitable for a range of advanced applications. Among the various synthesis techniques, chemical vapor deposition (CVD) is particularly effective for doping because it allows precise control over the growth conditions and dopant incorporation, outperforming other synthesis strategies in terms of scalability, uniformity, and clean growth. This review examines how solid, liquid, and gaseous precursor types play crucial roles in CVD doping, directly affecting the growth dynamics, doping efficiency, and material quality. By analyzing the mechanisms associated with each precursor form, this review highlights how these strategies address the challenges of achieving consistent and high-quality doped graphene. This discussion provides valuable insight into advancing CVD techniques for producing doped graphene with enhanced properties for cutting-edge applications.

Tar, a by-product of gasification, is a complex mixture of high molecular weight hydrocarbons that can cause significant damage to downstream equipment and reduce the efficiency of gas utilization. Effective tar destruction is therefore essential for producing clean syngas. Non-thermal plasmas (NTP's) technology offers a promising solution for gas cleaning by effectively destroying tar. This review explores various plasma sources and experimental approaches for using NTP's in tar destruction. It evaluates the performance of different plasma sources on the destruction of toluene and naphthalene, the most prevalent tar compounds in gasifier product gas, and discusses the chemical mechanisms and modeling approaches involved in their destruction. The most common modeling approach includes reaction kinetics, demonstrating how chemical reactions occur and behave in the NTP's system. This approach, known as the plasma global model, simplifies plasma modeling by focusing on reaction rates to predict the production and loss of species without needing to model plasma's bulk properties. The works that investigated plasma-catalysis for tar destruction were considered. A comparison of literature works reveals that the best performance for naphthalene destruction is achieved by corona plasma and reverse vortex flow gliding arc reactors, with the destruction efficiency $({\eta _d})$ of 99% and 99.8% at concentrations of 5 g m⁻³ and 10.3 g m⁻³, respectively. For toluene, the gliding arc discharge and rotating gliding arc combined with the catalyst demonstrate the highest efficiency, achieving 99% and 99.9% destruction at 22.9 g m⁻³ and 4 g m⁻³, respectively. The synergy between plasma and catalysts offers key benefits, including higher energy efficiency, faster reactions, and lower operating temperatures compared to traditional thermal methods. The review suggests that NTP's technology shows strong potential for removing biomass tar from gasification. It could be a promising solution for biomass tar cracking and upgrading product gas in real gasification applications. Several pilot and small-scale plasma plants have been developed, but the technology is still emerging and faces various technical and economic challenges.

Atmospheric pressure plasma jets operated in an ambient environment are known to generate a rich mixture of reactive oxygen species and reactive nitrogen species, collectively referred to as RONS. At the cellular level, RONS have been linked to well-established signaling pathways that are important in tackling disease. However, there are still major gaps in our knowledge of which RONS (speciation, dose, and depth) are delivered by plasma into tissue; and following on from this, how we can control the plasma to deliver RONS effectively and safely into tissue. The purpose of this topical review is to highlight the research achievements that have helped improve our understanding of the physical and chemical mechanisms underpinning the plasma jet production of RONS and how to control their delivery into biological systems. The review also identifies new research ideas to address gaps in our knowledge (of RONS generation and delivery) to tailor the next generation of plasma jets to deliver RONS into human tissue with the precision needed to realize the full clinical potential of the technology. Completing these gaps in our knowledge is vital for the future development of medical plasma technologies; and will improve the possibility of developing optimal plasma technologies and protocols tailored specifically for the requirements of each patient.

As an inherent and important property of light, polarization could provide information beyond light intensity and spectrum. However, traditional polarization detectors require bulky polarization optics and accurate heterogeneous integration, which limits their miniaturization. Conversely, recently developed miniaturized near-field polarization photodetectors can efficiently achieve detection with the advantages of being filterless, cost-effective, and portable. These attributes play a significant role in various fields, including astronomy, quantum optics, and medical diagnosis. In this paper, we review the progress of miniaturized near-field polarization photodetectors, including polarization photodetectors based on the nanowire, two-dimensional materials, chiral materials, and metasurface. Furthermore, this review analyzes the detection mechanisms of these photodetectors and provides a comprehensive summary of their operational wavelengths, photo responsivities, and polarization sensitivities, including polarization ratio for linear polarization and asymmetric ratio for circular polarization. Finally, the applications of near-field polarization photodetector are reviewed to highlight its potential in broad aspects of applications.

Nanosecond lasers are widely used in industrial applications as they are relatively inexpensive, and their compactness and robustness are an advantage. Much experimental work has been carried out to understand deeper the interaction between the nanosecond laser pulses and the targets, as these are complex, transient processes with spatial inhomogeneities. Beside the experiments, the modeling and numerical simulation on the laser interaction with the target are also crucial for understanding the dynamics of laser-material interactions and for optimizing laser processing applications. In this review, the progress of numerical modeling and simulation on nanosecond laser-target interactions are summarized from the aspects of laser-target interactions and target-plasma interface, laser-plasma interactions and plasma radiation, and numerical models on different scales with artificial intelligence advancing. The laser ablation, mass and energy transfer, and mechanical coupling are discussed in the aspect of the nanosecond laser-target interactions and target-plasma interface. The plasma expansion, plasma ionization and recombination, and plasma radiation are discussed in the aspect of the nanosecond laser-plasma interactions and plasma radiation. Then the numerical advances, including microscopic approaches based on molecular dynamics, mesoscopic approaches based on kinetic and statistical physics, macroscopic approaches based on fluid dynamics, and numerical simulations with machine learning are discussed. Finally, the challenges currently being encountered by numerical modeling and simulation on nanosecond laser-target interactions and its potential development direction are considered.

Metabolic heterogeneity within tumors is a key driver of drug resistance, as distinct subpopulations adapt to the tumor microenvironment by exploiting specific metabolic pathways. This diversity enables certain subpopulations to evade therapeutic intervention, thereby leading to therapy failure and cancer relapse. Metabolic reprogramming exacerbates resistance by enabling cancer cells to modulate their metabolic pathways to counteract therapeutic pressures, promoting the survival of resistant subpopulations. Traditional metabolic analyses generally measure average metabolite levels across cell populations, while Raman metabolic imaging offers a more precise, subcellular perspective, enabling non-destructive and real-time monitoring of dynamic metabolic processes related to drug resistance. Our review highlights advancements in Raman spectroscopy and microscopy, and explores their applications in cancer drug resistance research. We focus on their role in revealing intratumoral metabolic heterogeneity, monitoring metabolic reprogramming in drug-resistant cells, and enabling rapid cancer drug sensitivity evaluation.

The nonequilibrium nature of low-temperature plasmas typically requires the use of high-fidelity computational models for resolving electron kinetics, e.g., electron energy distribution functions (EEDFs), which affect the reactions, transport, and dynamics of low temperature plasmas. The present work proposes how data assimilation (DA) using the combination of physical-based models, such as collisional-radiative models, and measurement data obtained from optical emission spectroscopy can provide a computationally efficient means to estimate the underlying EEDFs in an argon plasma discharge over a wide range of pressures. The DA-based framework that employs an ensemble Kalman filter captures the non-Maxwellian EEDFs within a computational run time of minutes, which is multiple orders of magnitude more efficient than using a high-fidelity computational model. In addition, the estimated states are in good agreement with another set of measurements obtained from laser absorption spectroscopy, validating the DA approach for estimating the underlying physical processes in reactive, rarefied, ionized gases.

Triangular defects in 4H silicon carbide (4H-SiC) epitaxial layers, as well as the surrounding dislocation half-loop array generated by basal plane dislocations (BPD) have been directly visualized by photo-electrochemical (PEC) etching in a 0.05 M KOH solution at a corrosion current density of 25 mA/mm². Raman spectroscopy has revealed the presence of the tensile stress within the triangular defect. Direct observation of threading edge dislocation (TED) pairs adjacent to the triangular defect has also achieved by the PEC etching. The TED pairs in the dislocation half-loop array have been found to form during the epitaxy of 4H-SiC rather than being inherited from the substrate, as confirmed by two-photon excited fluorescence (TPEF). Our work indicates that the internal stress originating from triangular defects plays a critical role in the nucleation and slip of BPD half-loop and in the formation of the dislocation half-loop array. Our findings suggest that suppressing the formation of triangular defects and the dislocation half-loop arrays they induced can significantly reduce the BPD density in thick epitaxial layers, thereby minimizing bipolar degradation failures in neighboring devices and improving yield.

In order to further understand the formation and development process of cathode spot crater on Copper-Chromium (CuCr) nanocrystalline alloy electrode in vacuum arc, a new simulation method considering the distribution of different components is proposed. And a two-dimensional (2D) axisymmetric model is established to study the effects of different components on the formation and development of cathode spot crater. The differences in physical properties are considered in the model, and the interface between the Cu component and the Cr component is effectively tracked. The distribution, flow, and heat transfer of the Cu and Cr components are simulated. To directly demonstrate the advantages of the method, the simulation results are compared with those adopting the method that linearly combines the physical property parameters according to the weight percentage of components. Simulation result shows that the presence of Cr components has an important influence on the formation and development of cathode spot crater on CuCr nanocrystalline alloy electrode. The effects of different weight percentages of Cr components on the formation and development of cathode spot crater on CuCr nanocrystalline alloys are also studied. The results indicate that with the improvement of Cr component weight percentages, the temperature on the cathode spot crater is increased, and the fluidity of liquid metal is reduced during erosion. Finally, the simulation results have been compared with experimental results of other researchers.

The ever-increasing demand for high-quality hydrogen drives a strong emphasis on developing high-efficiency membranes for hydrogen purification and separation. By adopting a combined approach of first-principles calculations and molecular dynamics simulations, the structural stabilities and H2 purification and separation properties of strain modulated C7N6 monolayer membranes were performed. Our results show that when the applied biaxial tensile strains (BT-strains) are ≤12%, the C7N6 monolayer is still stable enough for applications. The H2 permeability of the C7N6 membrane under 5%-7% BT-strains separating from gas mixture (H2, O2, CO2, CO, N2 and H2O) can reach 2.51×107~5.83×107 GPU at 300 K, much higher than most of known membranes. H2, O2, CO2, CO, N2, and H2O molecules overcoming energy barrier of 0.145, 0.502, 0.574, 0.725, 0.798, and 0.587 eV, respectively, can pass through the 7%-strained C7N6. The selectivity of H2/O2, H2/H2O, H2/CO2, H2/CO, and H2/N2 at 300 K under 5% strain is 1.38×108, 2.65×1010, 2.25×1010, 1.17×1013, and 3.54×1014, respectively, which is 1-2 order of magnitude higher than that of the pure C7N6 membrane at 500 K, even under 7% strain, the corresponding selectivity reduces to 1.03×106, 2.71×108, 1.65×107, 5.53×109, and 9.39×1010, respectively. The 5%-7% strained C7N6 membranes were revealed to possess high-efficient H2 separation performance at room temperature. Our findings provide a favorable guidance for practical hydrogen separation and purification via strain-modulated C7N6 membranes.

Experimental evidence indicates that significant exchange coupling may exist between magnetic nanoparticles (MNPs) in dense MNP aggregates such as nanoflower clusters. Here, we examine the role of inter-particle exchange interactions in determining the magnetic properties of MNP clusters, in particular their athermal hysteresis in a low-frequency alternating field. We consider mechanically fixed close-packed clusters where each particle is modeled as a single macrospin coupled to the others by both dipolar interactions and nearest-neighbor exchange. Upon simulating the quasi-static hysteresis curves, we compute the loop area, remanent moment and coercive field, and we classify each curve by its shape. Computing curve types across parameter space reveals how their shape is determined by the interplay between exchange coupling, dipolar interactions, and uniaxial anisotropy. Strong exchange coupling produces fully saturated loops with coherent moment rotation. Moderate exchange and anisotropy result in magnetically soft clusters with high susceptibility. Finally, for complex clusters, weak to moderate exchange and strong anisotropy may produce highly irregular curves with several abrupt changes in magnetization. Our analysis demonstrates that exchange coupling between MNPs significantly increases the cluster energy product, thereby contributing to explain the exceptional heating power of nanoflowers.

Computationally efficient reduced-order plasma models, able to predict the plasmas behavior reliably and self-consistently, have remained unachievable so far. The necessity for these models has nonetheless continuously increased over the past decade for both fundamental studies and engineering applications. With the increase in computational power in recent years, and the emergence of several approaches that lower the computational burden of generating extensive high-fidelity plasma datasets, data-driven dynamics discovery methods can play a transformative role toward the realization of predictive, generalizable, and interpretable reduced-order models (ROMs) for plasma systems. In this work, we introduce a novel data-driven algorithm – "Phi Method" – for discovery of discretized systems of differential equations describing the dynamics. The success and generalizability of Phi Method roots in its constrained regression on a library of candidate terms that is informed by numerical discretization schemes. Phi Method's performance is first demonstrated for a one-dimensional plasma problem representative of the discharge evolution along the azimuthal direction of a typical Hall thruster. Next, we assess Phi Method's application toward parametric dynamics discovery, i.e., deriving models that embed parametric variations of the dynamics and in turn aim to provide faithful predictions of the systems' behavior over unseen parameter spaces. In terms of salient results, we observe that Phi-Method-derived ROM provides remarkably accurate predictions of the evolution dynamics of the involved plasma state variables. The parametric Phi Method is further able to well recover the governing parametric PDE for the adopted plasma test case and to provide accurate predictions of the system dynamics over a wide range of test parameters.

This paper computationally investigates partial discharges (PDs) in the form of self-sustained gas discharges.
 It presents two methods for predictive modeling:
 1) A new low-fidelity algorithm for the partial discharge inception voltage (PDIV) is introduced.
 The method is volume-resolved and describes both the strength of the self-sustained Townsend mechanism as well as the conventional streamer (or bulk) mechanism.
 It also instrinsically computes the inception region, i.e., the region where a first electron also leads to a discharge.
 2) We apply a high-fidelity plasma model based on kinetic Monte Carlo, which self-consistently resolves the plasma dynamics during the PD process.
 The two models are complementary in the sense that the low-fidelity model provides the when and where the PD occurs, while the high-fidelity model resolves the PD process itself, starting from the first electron.
 Prediction and quantification of the PD processes is provided for four application cases: 1) Protrusion-plane gaps, 2) spherical voids, 3) twisted wire pairs, and 4) triple junctions.
 Validation of the low-fidelity method is done through comparison with published experiments (where available), as well as virtual verification through comparison with the high-fidelity plasma model.

Ten years after its invention, g³— the mixture of C₄F₇N, CO₂ and O₂— remains the most promising candidate for the substitution of SF₆ as an insulating and switching gas. The extension and optimization of g³ applications at a high-voltage level, especially in circuit breakers, requires knowledge of the properties and dynamics of arc discharges in this gas. The features of the arc plasma can be obtained from appropriate magneto-hydrodynamic simulations. To predict the arc behaviour, a consistent set of thermo-physical and radiation properties is necessary. The corresponding database is calculated for the case of local thermodynamic equilibrium and covers the temperature range 250–40 000 K, the pressure range 0.01–150 bar and various contents of C₄F₇N, O₂ and Teflon (nozzle material). This contribution presents examples of plasma composition, thermo-physical and radiation properties for g³ mixtures. The feasibility of the new database is demonstrated on an example of simulation results for a g³-filled high-voltage circuit breaker. Predicted arc voltage and pressure build-up in selected volumes are compared with available experimental data. Furthermore, to explore the differences in switching behaviour between g³ and SF₆, simulations for these two gases at the same operation conditions were performed. The space- and time-dependent temperature and pressure profiles are presented and discussed.

The "straintronic magnetic tunnel junction" (s-MTJ) is a magnetic tunnel junction (MTJ) whose resistance state can be changed continuously or gradually from high to low, or vice versa, with a gate voltage that generates strain in the magnetostrictive soft layer. This unusual feature, not usually available in MTJs that are switched abruptly with spin transfer torque, spin–orbit torque or voltage-controlled-magnetic-anisotropy, enables many analog applications where the typically low tunneling magneto-resistance ratio of MTJs (i.e., the on/off ratio of the switch) and the relatively large switching error rate are not serious impediments unlike in digital logic or memory. More importantly, the transfer characteristic of a s-MTJ (conductance versus gate voltage) always sports a linear region that can be exploited to implement analog arithmetic, vector matrix multiplication and linear synapses in deep learning networks very effectively. In these applications, the s-MTJ is actually superior to the better known memristors and domain wall synapses which do not exhibit the linearity and/or the analog behavior¹.

The transition dynamics from the electrostatic to electromagnetic (E–H) coupling in a 2.45 GHz excited inductively coupled plasma (ICP) source is investigated using a set of microwave time-resolved records at different frequencies. This method, coming from semiconductor physics, has been newly adapted for plasma investigations. Nitrogen and oxygen plasmas have been analyzed in the range 20–1000 Pa with a constant excitation power of 40 W. With a resolution better than 100 ns, one can identify, depending on pressure, the coexistence of the E and H modes, a hybrid EH mode, and, in the case of oxygen plasma, the transition from negative to positive ions. The E–H transition time increases with pressure.

CO₂ has recently emerged as a leading candidate in the search for a more environmentally friendly alternative to SF₆. In high voltage circuit breakers, where the gas serves dual roles as both gaseous insulation and switching medium, commercially available solutions use CO₂ either alone or as the primary component of a mixture. In order for breakers to reach the level of performance necessary to support growing energy demands, a thorough understanding of the transient arc properties of CO₂ during the current interruption process is urgently needed. In support of these efforts, an experimental puffer circuit breaker has been developed to study thermal interruption in these gases with a high degree of control, under conditions otherwise comparable to a commercial breaker exposed to short-line fault-like current and voltage stresses. This test setup was used together with a suite of optical diagnostics including high-speed imaging and optical emission spectroscopy, with the main goal of measuring the temporal evolution of a CO₂ arc's radial temperature profile in the stagnation region near the instant of current interruption. To achieve this goal, a novel diagnostic technique—intensified video optical emission spectroscopy—has been applied for detailed analysis of plasma properties. This advanced technique allows for evaluation of time-dependent temperature decay with microsecond resolution through current zero, which is not possible with other emission spectroscopy methods. This method has allowed for the time-evolution of the decaying arc's temperature profile to be characterized for the first time throughout the decisive period surrounding current zero, under conditions relevant for thermal current interruption in commercial high voltage circuit breakers. Results showing temporal evolution can be obtained within one shot, and thus be separated from effects of nozzle aging. Results showed little temperature variation more than 50 µs before current zero, with changes in current mainly found to affect the arc cross-section. Fast temperature decay was limited to a period within 20 µs of current zero, when maximum temperatures were found to drop from 12 000 K to 7000 K, with a stable temperature near 6000 K measurable up to 40 µs after current interruption. The results also revealed the influence of nozzle erosion on the arc temperature profile, showing that fresh nozzles provide stronger cooling, resulting in a higher temperature, more constricted arc.

This paper reports new methods for reducing the density of structural defects on the surfaces of silicon p-i-n photodiodes. We experimentally proved that removing the silicon layer with packing defects, which are formed during oxidation, significantly contributes to reducing the density of the dislocations generated after phosphorus diffusion. Compared with the classical diffusion-planar technology for the preparation of silicon-based p-i-n photodiodes, our methods improve the parameters of photodiodes, especially dark current, sensitivity, noise equivalent power etc. Removal of the silicon layer is realized by carrying out chemical-dynamic polishing of the wafers after thermal oxidation in the areas of responsive elements. Although the optimum thickness of the etched silicon layer is about 2 μm, which corresponds to an etching time of 30 s, even taking into account the fact that the detector properties will deteriorate with increasing etching time, the change in the "maximum spectral response" is positive. It is demonstrated that the chemical-dynamic polishing is a powerful tool to modify spectral characteristics of photodiode sensitivity. The maximum of the spectral response can be accurately tailored in a wide spectral range from by changing the CDP processing parameters.

The improved properties of core–shell nanoparticles (CSNPs) over homogeneous nanoparticles (NPs) have expanded and diversified the applications of these nanomaterials. However, controlling the properties of CSNPs can be a challenging task. Low temperature plasmas have proven to be an effective method of producing NPs with uniform size and morphology, and high yield. That said, NP transport and growth dynamics are sensitive to LTP properties. We report on a computational investigation of the evolution of Ge–Si CSNP properties as a function of operating conditions through the modeling of a flowing, two-zone inductively coupled plasma (ICP) reactor. Ar/GeH₄ and Ar/SiH₄ gas mixtures were supplied to separate plasma zones at a pressure of 1 Torr to promote growth of Ge cores and Si shells. The negatively charged CSNPs are trapped electrostatically in the vicinity of the antennas where the plasma is generated and where the majority of particle growth occurs. Particles that grow to a critical size are then de-trapped by fluid drag due to neutral gas flow. A two-dimensional hybrid plasma model coupled with a three-dimensional kinetic NP transport model were utilized to resolve plasma chemistry and NP growth processes that take place on distinct timescales. The trends in CSNP properties and trapping mechanisms associated with flow rate, applied ICP power and inlet precursor fraction are discussed. While the spatial distribution of plasma produced radical species can have significant impact on the NP growth process, the NP transport dynamics are what ultimately dictates the growth environment that is unique to each particle and so determines their final dimension and composition. The key to optimizing reactor conditions involves controlling the spatial density of growth species and plasma profile as a means to tailor particle trapping dynamics suitable to produce CSNPs for a specific application.

Understanding the processes of axonal growth and pathfinding during cortical folding in the brain is crucial to unravel the mechanisms underlying brain disorders that disturb connectivity throughout human brain development. However, this topic remains incompletely understood, highlighting the need for further investigation. Here, we propose and evaluate a diffusion based-mechanistic model to understand how axons grow and navigate in the folding brain. To do so, a bilayer growth model simulating the brain was devised involving a thin gray matter overlying a thick white matter. Innovatively, the stochastic model of axonal growth was linked with the stress and deformation fields of the folding bilayer system. The results showed that the modulus ratio of the gray matter to the white matter and the axonal growth rate are two potentially critical parameters that significantly influence axon pathfinding in the folding brain. The model demonstrated robust predictability in identifying axonal termination points and offered a potential mechanism explaining why axons settle more in gyri (ridges) than sulci (valleys) of the brain. Importantly, the results explain how alterations in the mechanical properties of the folding system can impact the underlying connectivity patterning. This mechanistic insight not only enhances our understanding of brain connectivity development during the fetal stage but also sheds light on brain disorders characterized by linked abnormalities in cortical folds and disruptions in connectivity.