Local Trapping Research Articles

Abstract C014: Degradation of extracellular trap DNA sustains anti-tumor immune responses in breast cancer

Abstract Increased extracellular DNA (exDNA) levels in the blood are a hallmark of metastatic cancer, arising from tumor lysis, apoptosis, necrosis, and neutrophil extracellular traps (NETs) released by tumor-expanded neutrophils. NETs, web-like chromatin structures extruded by neutrophils to trap pathogens, are highly immunogenic due to their DNA and histone content and their persistent interaction with dendritic cells. Tumor cells can also release extracellular traps. In myeloid leukemia, blasts release nuclear content via traps to activate coagulation or sustain myeloproliferation. Extracellular traps have been found in NPM1 mutant AML patients, with mutant NPM co-localizing with histones along exDNA traps. Forcing trap extrusion in NPM mutant leukemia cells has been used to create dendritic cell-based vaccines that break tolerance against NPMc antigens. This suggests that NETs in the tumor microenvironment (TME) may promote tolerance, with breaking tolerance requiring consistent NET formation and dendritic cell interaction. NETs are also seen in solid tumors, such as triple-negative breast cancer (TNBC), where they aid in cancer cell migration, invasion, and awakening of circulating tumor cells. However, the capacity of tumor cells to directly extrude DNA traps is not fully understood. To investigate this, various human and murine breast cancer cell lines were seeded on poly-D-lysine coated slides, stained with DAPI and Sytoxgreen, and observed via confocal microscopy. Tumor cells extruded DNA traps within 4 hours without stimuli. Notably, trap extrusion correlated with cell line aggressiveness and was abolished by DNase, indicating dsDNA composition. The absence of traps in 24-hour cultures suggests DNA extrusion may help rare tumor cells survive and proliferate. In line, DNase treatment reduced tumor proliferation and increased apoptosis. To assess the significance of DNA traps in vivo, BALB/c mice were injected with the highly metastatic 4T1 clone 5 breast cancer model, with or without DNase treatment, and monitored starting 5 days post-injection and weekly until day 28. Histological analysis at the injection site 5 days post-injection showed that DNase treatment reduced both local trap formation and tumor cell proliferation, as indicated by BrdU incorporation. Moreover, immunohistochemistry revealed that in DNase-treated mice, initial tumor growth was followed by complete tumor regression, accompanied by local and systemic CD8 T cell activation. Correspondingly, although a few metastases were detectable at day 14 in DNase-treated mice, no metastases were observed by day 28. Although it remains unclear whether tumor-derived or immune cell-derived traps are most relevant in our model, the results demonstrate that their presence in the TME promotes tolerance despite the inherent immune adjuvant properties of the traps. This suggests that DNase treatment could be particularly effective in combating early tumor development or relapse when used as part of a combination therapy designed to sustain anti-tumor immune responses. Citation Format: Sabina Sangaletti, Paola Portararo, Laura Botti, Valeria Cancila, Claudio Tripodo, Mario P. Colombo, Claudia Chiodoni. Degradation of extracellular trap DNA sustains anti-tumor immune responses in breast cancer [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Tumor-body Interactions: The Roles of Micro- and Macroenvironment in Cancer; 2024 Nov 17-20; Boston, MA. Philadelphia (PA): AACR; Cancer Res 2024;84(22_Suppl):Abstract nr C014.

An effective Key Frame Extraction technique based on Feature Fusion and Fuzzy-C means clustering with Artificial Hummingbird

Key frame extraction is very important in video summarization and content-based video analysis to address the problem of data redundancy in a video. Key frame extraction enables quick navigation and expert video arrangement in many applications. The visually impaired can benefit from the use of key frame extraction for rapid object recognition and tracking. Most key frame extraction techniques consider only a single visual feature instead of multiple features or full pictorial information of the video. This study proposes a key frame extraction method from a video that (i) first removes insignificant frames by pre-processing, (ii) second, four visual and structural feature differences among the consecutive frames are extracted and aggregated to identify informative frames, (iii) third, to cluster the obtained frames, a hybrid FCM-AHA method is proposed by combining Fuzzy C-means(FCM) with artificial hummingbird optimization algorithm (AHA) to circumvent the local minima trapping problem of FCM, and finally, from each cluster, the two frames having greatest Euclidean distance from all the other frames within a cluster is selected as key frames to remove redundant frames. Experimental results on the Open video and YouTube datasets show that the suggested method outperforms state-of-the-art methods both in terms of subjective qualitative analysis and objective quantitative evaluation, e.g., Precision, Recall, and F-score. Further, results are also taken on real video to demonstrate its applicability in real-life applications.

Optimization of vehicle conceptual design problems using an enhanced hunger games search algorithm

Abstract Electric vehicles have become a standard means of transportation in the last 10 years. This paper aims to formalize design optimization problems for electric vehicle components. It presents a tool conceptual design technique with a hunger games search optimizer that incorporates dynamic adversary-based learning and diversity leader (referred to as HGS-DOL-DIL) to overcome the local optimum trap and low convergence rate limitations of the Hunger Games search algorithm to improve the convergence rate. The performance of the proposed algorithms is studied on six widely used engineering design problems, complex constraints, and discrete variables. For the HGS-DOL-DIL practical feasibility analysis, a case study of shape optimization of an electric car suspension arm from the industry is carried out. Overall, the inclusion of the OL strategy has proven its superiority in solving real-world problems, especially in solving real-world problems such as shape optimization of an electric vehicle automobile suspension arm, showing that the algorithm improves the search space improves the solution quality, and reflects its potential to find global optimum solutions in a well-balanced exploration and exploitation phase.

Ultra-broadband underwater meta-absorber with gradient impedance-matched composite material

Abstract We have developed an underwater acoustic meta-absorber that exhibits ultra-broadband absorption, with most of absorption coefficients exceeding 0.9 across the frequency range of 1 kHz−20 kHz. This performance is achieved through the localized trapping of acoustic waves and efficient energy dissipation, facilitated by local resonances within meticulously designed, impedance-matched composite materials. The designed gradient structure of the absorber allows for a reduced width of 84 mm (0.05λ at 1 kHz) and a customizable length, determined by the number of unit cells, each with a length of 15 mm (0.01λ at 1 kHz). This different design approach yields an ultra-broadband meta-absorber of compact dimensions, offering a promising alternative for the development of underwater absorption metamaterials and their practical applications.

Multi-strategy fusion improved Northern Goshawk optimizer is used for engineering problems and UAV path planning

Addressing the imbalance between exploration and exploitation, slow convergence, local optima Traps, and low convergence precision in the Northern Goshawk Optimizer (NGO): Introducing a Multi-Strategy Integrated Northern Goshawk Optimizer (MINGO). In response to challenges faced by the Northern Goshawk Optimizer (NGO), including issues like the imbalance between exploration and exploitation, slow convergence, susceptibility to local optima, and low convergence precision, this paper introduces an enhanced variant known as the Multi-Strategy Integrated Northern Goshawk Optimizer (MINGO). The algorithm tackles the balance between exploration and exploitation by improving exploration strategies and development approaches. It incorporates Levy flight strategies to preserve population diversity and enhance convergence precision. Additionally, to avoid getting trapped in local optima, the algorithm introduces Cauchy mutation strategies, improving its capability to escape local optima during the search process. Finally, individuals with poor fitness are eliminated using the crossover strategy of the Differential Evolution algorithm to enhance the overall population quality. To assess the performance of MINGO, this paper conducts an analysis from four perspectives: population diversity, balance between exploration and exploitation, convergence behavior, and various strategy variants. Furthermore, MINGO undergoes testing on the CEC-2017 and CEC-2022 benchmark problems. The test results, along with the Wilcoxon rank-sum test results, demonstrate that MINGO outperforms NGO and other advanced optimization algorithms in terms of overall performance. Finally, the applicability and superiority of MINGO are further validated on six real-world engineering problems and a 3D Trajectory planning for UAVs.

Advanced spectroscopic methods for probing in-gap defect states in amorphous SiNx for charge trap memory applications

Silicon nitride (SiNx) serves as the charge trap layer in current 3D NAND flash memory devices. The precise formation mechanism and electronic structure of localized defect trap states in SiNx remain elusive. Here, we present a refined experimental methodology to elucidate the in-gap defect states and the band gaps in amorphous SiNx thin films. Our approach integrates high-resolution reflection electron energy loss spectroscopy (REELS) and spectroscopic ellipsometry (SE) for comprehensive analysis. By systematical analysis, we aim to provide a robust method for determining in-gap electronic states in SiNx. We investigated two different SiNx films prepared by plasma-enhanced chemical vapor deposition and sputtering. Our analysis revealed several distinct in-gap states and determined band gap energies. This approach not only provide advanced spectroscopic methods to characterize the defect electronic states in SiNx, but also applicable to other large band gap semiconductors or dielectrics to predict device-level characteristics for future devices.

From Ultrafast Photoinduced Small Polarons to Cooperative and Macroscopic Charge-Transfer Phase Transition.

We study by femtosecond infrared spectroscopy the ultrafast and persistent photoinduced phase transition of the Rb0.94Mn0.94Co0.06[Fe(CN)6]0.98 ⋅ 0.2H2O material, induced at room temperature by a single laser shot. This system exhibits a charge-transfer based phase transition with a 75 K wide thermal hysteresis, centred at room temperature, from the low temperature Mn3+-N-C-Fe2+ tetragonal phase to the high temperature Mn2+-N-C-Fe3+ cubic phase. At room temperature, the photoinduced phase transition is persistent. However, the out-of-equilibrium dynamics leading to this phase is multi-scale. Femtosecond infrared spectroscopy, particularly sensitive to local reorganizations through the evolution of the frequency of the N-C vibration modes with the different characteristic electronic states, reveals that at low laser fluence and on short time scale, the photoexcitation of the Mn3+-N-C-Fe2+ phase creates small charge-transfer polarons [Mn2+-N-C-Fe3+]* within ≃250 fs. The local trapping of photoinduced intermetallic charge-transfer is characterized by the appearance of a polaronic infrared band, due to the surrounding Mn2+-N-C-Fe2+ species. Above a threshold fluence, when a critical fraction of small CT-polarons is reached, the macroscopic phase transition to the persistent Mn2+-N-C-Fe3+ cubic phase occurs within ≃ 100 ps. This non-linear photo-response results from elastic cooperativity, intrinsic to a switchable lattice and reminiscent of a feedback mechanism.

From Ultrafast Photoinduced Small Polarons to Cooperative and Macroscopic Charge‐Transfer Phase Transition

AbstractWe study by femtosecond infrared spectroscopy the ultrafast and persistent photoinduced phase transition of the Rb0.94Mn0.94Co0.06[Fe(CN)6]0.98 ⋅ 0.2H2O material, induced at room temperature by a single laser shot. This system exhibits a charge‐transfer based phase transition with a 75 K wide thermal hysteresis, centred at room temperature, from the low temperature Mn3+−N−C−Fe2+ tetragonal phase to the high temperature Mn2+−N−C−Fe3+ cubic phase. At room temperature, the photoinduced phase transition is persistent. However, the out‐of‐equilibrium dynamics leading to this phase is multi‐scale. Femtosecond infrared spectroscopy, particularly sensitive to local reorganizations through the evolution of the frequency of the N−C vibration modes with the different characteristic electronic states, reveals that at low laser fluence and on short time scale, the photoexcitation of the Mn3+−N−C−Fe2+ phase creates small charge‐transfer polarons [Mn2+−N−C−Fe3+]* within ≃250 fs. The local trapping of photoinduced intermetallic charge‐transfer is characterized by the appearance of a polaronic infrared band, due to the surrounding Mn2+−N−C−Fe2+ species. Above a threshold fluence, when a critical fraction of small CT‐polarons is reached, the macroscopic phase transition to the persistent Mn2+−N−C−Fe3+ cubic phase occurs within ≃ 100 ps. This non‐linear photo‐response results from elastic cooperativity, intrinsic to a switchable lattice and reminiscent of a feedback mechanism.

Implementation of Chernobyl optimization algorithm based feature selection approach to predict software defects

Background Software defects can have catastrophic consequences. Therefore, fixing these defects is crucial for the evolution of software. Software Defect Prediction (SDP) enables developers to investigate unscramble faults in the inaugural parts of the software progression mechanism. However, SDP faces many challenges, including the high magnitude of attributes in the datasets, which can degrade the prognostic performance of a defect forecasting model. Feature selection (FS), a compelling instrument for overcoming high dimensionality, selects only the relevant and best features while carefully discarding others. Over the years, several meta-heuristic algorithms such as the Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Differential Evolution (DE), and Ant Colony Optimization (ACO) have been used to develop defect prediction models. However, these models suffer from several drawbacks, such as high cost, local optima trap, lower convergence rate, and higher parameter tuning. To overcome the above shortcomings, this study aims to develop an innovative FS technique, namely, the Chernobyl Optimization Algorithm (FSCOA), to unwrap the most informative features that can produce a precise prediction model while minimizing errors. Methods The proposed FSCOA approach mimicked the process of nuclear radiation while attacking humans after an explosion. The proposed FSCOA approach was combined with four widely used classifiers, namely Decision Tree (DT), K-nearest neighbor (KNN), Naive Bayes (NB), and Quantitative Discriminant Analysis (QDA), to determine the finest attributes from the SDP datasets. Furthermore, the accuracy of the recommended FSCOA method is correlated with existing FS techniques, such as FSDE, FSPSO, FSACO, and FSGA. The statistical merit of the proposed measure was verified using Friedman and Holm tests. Results The experimental findings showed that the proposed FSCOA approach yielded the best accuracy in most cases and achieved an average rank of 1.75, followed by the other studied FS approaches. Furthermore, the Holm test showed that the p-value was lower than or equivalent to the value of α/(A-i), except for the FSCOA and FSGA and FSCOA and FSACO models. Conclusion The experimental findings showed that the prospective FSCOA procedure eclipsed alternative FS techniques with higher accuracy in almost all cases while selecting optimal features.

Constructing local nanomolecular trap in a scalable, low-cost, and ultramicroporous metal–organic framework for efficient capture of greenhouse gases SF6 and CO2

The capture of sulfur hexafluoride (SF6) and carbon dioxide (CO2) is an important but challenging task for reducing greenhouse gas emission and thereby diminishing negative impacts of global climate change. Herein, we integrated local nanomolecular traps into an inexpensive cobalt-based metal–organic framework (MOF, namely GNU-3) with suitable pore sizes and functional pore surfaces for capturing greenhouse gases. Activated GNU-3a showed good adsorption and separation properties for SF6 and CO2. Especially for SF6, GNU-3a displayed the high SF6/N2 (10:90, v/v) selectivity of 317.6 and SF6 uptake capacity of 2.63mmol/g at 298 K and 1.0 bar. Grand canonical Monte Carlo simulations and first-principles dispersion-corrected density functional theory calculations identified that gas molecules were favorably trapped in polyaromatic ring functionalized channel centers and nanomolecular traps of pore walls by the strong hydrogen bond interactions. Excellent breakthrough results recommend practical separating properties of GNU-3a for binary SF6/N2 and CO2/N2 mixtures, and ternary SF6/CO2/N2 mixtures. Moreover, benefitting from superior features of cheap raw materials and large-scale synthesis, GNU-3 is expected to be applied to practical industrial scenarios. This work may provide insights into constructing prospectively industrial porous MOF adsorbents with optimal pore structures for greenhouse gas capture.

Continuous-time Hammerstein model identification utilizing hybridization of Augmented Sine Cosine Algorithm and Game-Theoretic approach

The widespread use of dynamic systems has greatly simplified various human-operated tasks. However, the complex and nonlinear nature of these systems has posed challenges in determining their structure due to heavy reliance on modeling for theoretical and empirical purposes in academic and practical contexts. This challenge is particularly evident in the Hammerstein model, which is well-known for its recognition of structural nonlinearity. To address the limitations of uncovering an optimized continuous-time Hammerstein model, researchers have explored the practical application of the Augmented Sine Cosine Algorithm-Game Theoretic (ASCA-GT). By combining the Game Theoretic (GT) and Sine Cosine Algorithm (SCA), this hybrid approach aims to achieve a nonlinear position-updated process for improved exploration and exploitation, while effectively overcoming issues like local optima trapping. The performance of ASCA-GT was evaluated using 13 benchmark functions and mathematical scenarios, as well as the Twin-Rotor System (TRS) and Electro-Mechanical Positioning System (EMPS). The effectiveness of the proposed technique was assessed using Wilcoxon's rank test, which confirmed the superior performance of ASCA-GT compared to other metaheuristic optimization methods in determining the continuous-time Hammerstein model.

Photodetectors Based on MASnI3/MoS2 Hybrid-Dimensional Heterojunction Transistors: Breaking the Responsivity-Speed Trade-Off.

Hybrid-dimensional heterojunction transistor (HDHT) photodetectors (PDs) have achieved high responsivities but unfortunately are still with unacceptably slow response speeds. Here, we propose a MASnI3/MoS2 HDHT PD, which exhibits the possibility to obtain high responsivity and fast response simultaneously. By exploring the detailed photoelectric responses utilizing a precise optoelectronic coupling simulation, the electrical performance of the device is optimally manipulated and the underlying physical mechanisms are carefully clarified. Particularly, the influence and modulation characteristics of the trap effects on the carrier dynamics of the PDs are investigated. We find that the localized trap effect in perovskite, especially at its top surface, is primarily responsible for the high responsivity and long response time; moreover, it is normally hard to break such a responsivity-speed trade-off due to the inherent limitation of the trap effect. By synergistically coupling the photogating effect, trap effect, and gate regulation, we indicate that it is possible to achieve an enhancement of the responsivity-bandwidth product by about 3 orders of magnitude. This study facilitates a fine modulation of the responsivity-speed relationship of hybrid-dimensional PDs, enabling breaking the traditional responsivity-speed trade-off of many PDs.

Tunable plasmonic tweezers based on nanocavity array structure for multi-site nanoscale particles trapping

The ability of plasmonic optical tweezers based on metal nanostructure to stably trap and dynamically manipulate nanoscale objects at low laser power has been widely used in the fields of nanotechnology and life sciences. In particular, their plasmonic nanocavity structure can improve the local field intensity and trap depth by confining electromagnetic fields to subwavelength volumes. In this paper, the R6G dye molecules with 10−6 M were successfully trapped by using the Ag@Polydimethylsiloxane nanocavity array structure, and a R6G micro-ring was formed under the combined action of plasmonic optical force and thermophoresis. Subsequently, the theoretical investigation revealed that the trapping performance can be flexibly adjusted by changing the structural parameters of the conical nanocavity unit, and it can provide a stable potential well for polystyrene particles of RNP = 14 nm when the cavity depth is 140 nm. In addition, it is found that multiple trapping sites can be activated simultaneously in the laser irradiation area by investigating the trapping properties of the hexagonal conical nanocavity array structure. This multi-site stable trapping platform makes it possible to analyze multiple target particles contemporaneously.

Parker Solar Probe Observations of Energetic Particles in the Flank of a Coronal Mass Ejection Close to the Sun

We present an event observed by Parker Solar Probe (PSP) at ∼0.2 au on 2022 March 2 in which imaging and in situ measurements coincide. During this event, PSP passed through structures on the flank of a streamer blowout coronal mass ejection (CME) including an isolated flux tube in front of the CME, a turbulent sheath, and the CME itself. Imaging observations and in situ helicity and principal variance signatures consistently show the presence of flux ropes internal to the CME. In both the sheath and the CME interval, the distributions are more isotropic, the spectra are softer, and the abundance ratios of Fe/O and He/H are lower than those in the isolated flux tube, and yet elevated relative to typical plasma and solar energetic particle abundances. These signatures in the sheath and the CME indicate that both flare populations and those from the plasma are accelerated to form the observed energetic particle enhancements. In contrast, the isolated flux tube shows large streaming, hard spectra, and large Fe/O and He/H ratios, indicating flare sources. Energetic particle fluxes are most enhanced within the CME interval from suprathermal through energetic particle energies (∼keV to >10 MeV), indicating particle acceleration, as well as confinement local to the closed magnetic structure. The flux-rope morphology of the CME helps to enable local modulation and trapping of energetic particles, in particular along helicity channels and other plasma boundaries. Thus, the CME acts to build up energetic particle populations, allowing them to be fed into subsequent higher-energy particle acceleration throughout the inner heliosphere where a compression or shock forms on the CME front.

Improved Snake Optimizer Using Sobol Sequential Nonlinear Factors and Different Learning Strategies and Its Applications

The Snake Optimizer (SO) is an advanced metaheuristic algorithm for solving complicated real-world optimization problems. However, despite its advantages, the SO faces certain challenges, such as susceptibility to local optima and suboptimal convergence performance in cases involving discretized, high-dimensional, and multi-constraint problems. To address these problems, this paper presents an improved version of the SO, known as the Snake Optimizer using Sobol sequential nonlinear factors and different learning strategies (SNDSO). Firstly, using Sobol sequences to generate better distributed initial populations helps to locate the global optimum solution faster. Secondly, the use of nonlinear factors based on the inverse tangent function to control the exploration and exploitation phases effectively improves the exploitation capability of the algorithm. Finally, introducing learning strategies improves the population diversity and reduces the probability of the algorithm falling into the local optimum trap. The effectiveness of the proposed SNDSO in solving discretized, high-dimensional, and multi-constraint problems is validated through a series of experiments. The performance of the SNDSO in tackling high-dimensional numerical optimization problems is first confirmed by using the Congress on Evolutionary Computation (CEC) 2015 and CEC2017 test sets. Then, twelve feature selection problems are used to evaluate the effectiveness of the SNDSO in discretized scenarios. Finally, five real-world technical multi-constraint optimization problems are employed to evaluate the performance of the SNDSO in high-dimensional and multi-constraint domains. The experiments show that the SNDSO effectively overcomes the challenges of discretization, high dimensionality, and multi-constraint problems and outperforms superior algorithms.

Mobile Robot Path Planning Algorithm Based on NSGA-II

Path planning for mobile robots is a key technology in robotics. To address the issues of local optima trapping and non-smooth paths in mobile robot path planning, a novel algorithm based on the NSGA-II (Non-dominated Sorting Genetic Algorithm II) is proposed. The algorithm utilizes a search window approach for population initialization, which improves the quality of the initial population. An innovative fitness function is designed as the objective function for optimization iterations. A probability-based selection strategy is employed for population selection and optimization, enhancing the algorithm’s ability to escape local minima and preventing premature convergence to suboptimal solutions. Furthermore, a path smoothing algorithm is developed by incorporating Bézier curves. By connecting multiple segments of Bézier curves, the problem of the high computational complexity associated with high-degree Bézier curves is addressed, while simultaneously achieving smooth paths. Simulation results demonstrated that the proposed path planning algorithm exhibited fewer iterations, superior path quality, and path smoothness. Compared to other methods, the proposed approach demonstrated better overall performance and practical applicability.

Covalently‐Bonded Diaphite Nanoplatelet with Engineered Electronic Properties of Diamond

AbstractDiamond, as a highly promising “extreme” semiconductor material, necessitates electronic property engineering to unleash its full potential in electronic and photonic devices. In this work, the diaphite nanoplatelet, consisting of (11) planes of diamond nanoplatelet covalently bonded with graphite (0001) planes, is facilely synthesized using one‐step microwave plasma enhanced chemical vapor deposition method. The high‐energy plasma created by the pillar plays a crucial role in the formation. Importantly, altered electronic and optical properties are determined in the diaphite nanoplatelet through electron energy loss spectrum, density functional theory calculations, and cathodoluminescence spectroscopy. It is revealed that the strong sp3/sp2‐hybridized interfacial covalent bonding in the diaphite nanoplatelet induces the electron transfer from diamond to graphite. This modulates the electronic structure of the near‐interface layer of diamond and triggers a new local trapping band below the conduction band minimum within the bandgap. Consequently, the covalently‐bonded diaphite exhibits a different optical emission characteristic ranging from 2.5 to 3.64 eV, featuring a significant peak blueshift of 430 meV compared to the H‐terminated diamond. This work demonstrates a novel method to engineer the electronic properties of diamond, opening avenues for functional semiconductor device applications of diamond.

Trade-off between local chemical order and lattice distortion in affecting dislocation motion in NbTiZr multi-principal element alloys

Local chemical order (LCO) is energetically favorable in multi-principal element alloys (MPEAs) due to the chemical affinity difference among constituent species. Here we show that the increase of LCO is often accompanied by a decrease of lattice distortion in body-centered-cubic (BCC) MPEAs. This leads to a trade-off between LCO and lattice distortion in their effects on retarding the glide of edge and screw dislocations. With a random solid solution without LCO as a reference, the emergence of LCO at the expense of lattice distortion could weaken the local trapping force for dislocation glide and promote dislocation mobility. However, when the LCO becomes sufficiently large and robust, it acts to hinder dislocation glide. We found that the mobility of edge dislocation is more affected by local lattice distortion, while the kink-pair based screw dislocation motion is more governed by LCO. We further quantitatively evaluate the contribution of LCO and lattice distortion to the overall critical shear stress for dislocation glide using analytical models. For the present equiatomic NbTiZr MPEAs, the critical shear stress is dominated by lattice distortion when the LCO is still limited to chemical short-range order (CSRO). However, LCO could even surpass lattice distortion to become a major contributor to the critical shear stress once it grows to a sufficiently large size. Our work indicates that increasing the degree and extent of LCO may provide an effective means to regulate dislocation motion, shedding light on how to use LCOs to improve the strength of BCC MPEAs.

Unveiling heterogeneity of hysteresis in perovskite thin films

The phenomenon of current–voltage hysteresis observed in perovskite-based optoelectronic devices is a critical issue that complicates the accurate assessment of device parameters, thereby impacting performance and applicability. Despite extensive research efforts aimed at deciphering the origins of hysteresis, its underlying causes remain a subject of considerable debate. By employing nanoscale investigations to elucidate the relationship between hysteresis and morphological characteristics, this study offers a detailed exploration of photocurrent–voltage hysteresis at the nanoscale within perovskite optoelectronic devices. Through the meticulous analysis of localized I–V curve arrays, our research identifies two principal hysteresis descriptors, uncovering a predominantly inverted hysteresis pattern in 87% of the locations examined. This pattern is primarily attributed to the energetic barrier encountered at the interface between the probe and the perovskite material. Our findings underscore the pronounced heterogeneity and grain-dependent variability inherent in hysteresis behavior, evidenced by an average Hysteresis Index value of 0.24. The investigation suggests that the localized hysteresis phenomena cannot be exclusively attributed to either photocharge collection processes or organic cation migration at grain boundaries. Instead, it appears significantly influenced by localized surface trap states, which play a pivotal role in modulating electron and hole current dynamics. By identifying the key factors contributing to hysteresis, such as localized surface trap states and their influence on electron and hole current dynamics, our findings pave the way for targeted strategies to mitigate these effects. This includes the development of novel materials and device architectures designed to minimize energy barriers and enhance charge carrier mobility, thereby improving device performance and longevity. This breakthrough in understanding the microscale mechanisms of hysteresis underscores the critical importance of surface/interface defect trap passivation in mitigating hysteretic effects, offering new pathways for enhancing the performance of perovskite solar cells.

Interfacial charge associated reliability improvement in arsenide/antimonide tunneling interfaced-junctionless TFET

This article focuses on the investigation of reliability improvement in the arsenide/antimonide tunable bandgap tunneling interfaced junctionless TFET (HD-HJLTFET) by introducing positive (donor) and negative (acceptor) localized interfacial trap charges (ITCs) at the semiconductor/oxide (S/O) and semiconductor/semiconductor (S/S) interface. The compound semiconducting materials, InAs (lower bandgap) and AlGaSb (higher bandgap) have been incorporated in the source and channel regions in the proposed device (HJLTFET). Further, to improve the device reliability against ITCs, a hetero dielectric engineered gated device has also been designed and analysed (HD-HJLTFET). In HD-HJLTFET, a high-k dielectric near source to channel (S/C) interface and low-k dielectric region towards drain to channel (D/C) interface has been used. It has been obtained that HD engineering enhances the microwave and harmonic distortion performance of HD-HJLTFET. It has been evaluated that HD-HJLTFET/ HJLTFET delivers ON current of 38.3 μA/14μA, transconductance 108 μS/23.2μS, cut-off frequency 805.8GHz/406.7GHz and gain of 223.5/197.4. Further results show that HD delivers gm2 (∼28% ↓), gm3 (∼9 times), second-order and third-order harmonic distortion (∼42% ↓ and ∼90% ↓), and total harmonic distortion (∼26% ↓) as compared to HJLTFET. The linearity parameters of HD-HJLTFET (VIP2, VIP3, IIP3, 1dB compression point, and IMD3) also showed marked improvement with negligible variation against different ITC polarity than its counter device, making it more reliable for low power microwave and distortion-free wireless communication systems.