Specific Configuration Research Articles

Li+ Quasi‐Grotthuss Topochemistry Transport Enables Direct Regeneration of Spent Lithium‐Ion Battery Cathodes

AbstractDirect regeneration of spent lithium‐ion batteries offers economic benefits and a reduced CO2 footprint. Surface prelithiation, particularly through the molten salt method, is critical in enhancing spent cathode repair during high‐temperature annealing. However, the sluggish Li+ transport kinetics, which predominantly relies on thermally driven processes in the traditional molten salt methods, limit the prelithiation efficiency and regeneration of spent cathodes. Here, we introduce a special molecular configuration (benzoate) into molten salts that facilitates rapid Li+ transport to the surface of LiNi0.5Co0.2Mn0.3O2 (NCM) via a quasi‐Grotthuss topochemistry mechanism. This approach effectively avoids the phase transitions that could adversely degrade the electrochemical performance due to insufficient lithiation during the repair process. Computational and experimental analyses reveal that the system enables fast Li+ migration through the topological hopping of benzoate in organic lithium salt, rather than relying solely on thermally driven diffusion, thereby significantly improving the prelithiation and repair efficiency of spent NCM cathodes. Benefiting from the quasi‐Grotthuss Li+ topochemistry transport, the degraded structure and Li vacancies in the spent cathode are effectively eliminated, yieding the regenerated cathode with good cycling stability comparable to commercial counterparts. The proposed Li+ transport mechanism presents a promising route for the efficient and sustainable regeneration of spent cathodes.

Li+ Quasi-Grotthuss Topochemistry Transport Enables Direct Regeneration of Spent Lithium-Ion Battery Cathodes.

Direct regeneration of spent lithium-ion batteries offers economic benefits and a reduced CO2 footprint. Surface prelithiation, particularly through the molten salt method, is critical in enhancing spent cathode repair during high-temperature annealing. However, the sluggish Li+ transport kinetics, which predominantly relies on thermally driven processes in the traditional molten salt methods, limit the prelithiation efficiency and regeneration of spent cathodes. Here, we introduce a special molecular configuration (benzoate) into molten salts that facilitates rapid Li+ transport to the surface of LiNi0.5Co0.2Mn0.3O2 (NCM) via a quasi-Grotthuss topochemistry mechanism. This approach effectively avoids the phase transitions that could adversely degrade the electrochemical performance due to insufficient lithiation during the repair process. Computational and experimental analyses reveal that the system enables fast Li+ migration through the topological hopping of benzoate in organic lithium salt, rather than relying solely on thermally driven diffusion, thereby significantly improving the prelithiation and repair efficiency of spent NCM cathodes. Benefiting from the quasi-Grotthuss Li+ topochemistry transport, the degraded structure and Li vacancies in the spent cathode are effectively eliminated, yieding the regenerated cathode with good cycling stability comparable to commercial counterparts. The proposed Li+ transport mechanism presents a promising route for the efficient and sustainable regeneration of spent cathodes.

Conceptual Design of a Novel Autonomous Water Sampling Wing-in-Ground-Effect (WIGE) UAV and Trajectory Tracking Performance Optimization for Obstacle Avoidance

As a fundamental part of water management, water sampling treatments have recently been integrated into unmanned aerial vehicle (UAV) technologies and offer eco-friendly, cost-effective, and time-saving solutions while reducing the necessity for qualified staff. However, the majority of applications have been conducted with rotary-wing configurations, which lack range and sampling capacity (i.e., payload), leading scientists to search for alternative designs or special configurations to enable more comprehensive water assessments. Hence, in this paper, the conceptual design of a novel long-range and high-capacity WIGE UAV capable of autonomous water sampling is presented in detail. The design process included a vortex lattice solver for aerodynamic investigations, while analytical and empirical methods were used for weight and dimensional estimations. Since the mission involved operation inside maritime traffic, potential obstacle avoidance scenarios were discussed in terms of operational safety, and the aim was for autonomous trajectory tracking performance to be improved by means of a stochastic optimization algorithm. For this purpose, an artificial intelligence-integrated concurrent engineering approach was applied for autonomous control system design and flight altitude determination, simultaneously. During the optimization, the stability and control derivatives of the constituted longitudinal and lateral aircraft dynamic models were predicted via a trained artificial neural network (ANN). The optimization results exhibited an aerodynamic performance enhancement of 3.92%, which indicates a remarkable improvement in trajectory tracking performance for both the fly-over and maneuver obstacle avoidance modes, by 89.9% and 19.66%, respectively.

Hybrid Robotic Arm for Autonomous Aerial Refueling

Autonomous aerial refueling technology is gaining increasing attention to enhance aircraft combat capabilities. Current research on autonomous refueling focuses mainly on flight control laws, with little attention to the automation of refueling pipes. This leads to high demands on control law performance and navigation accuracy, making it difficult to ensure reliability. To address this, we propose a robotic arm system capable of automatic docking during the flexible aerial refueling process. The system uses a conical kinematic space configuration, offering enhanced stability and impact resistance. The frame-type structure achieves a lightweight design. Additionally, we establish a single-objective optimization model for the connecting rod dimensions and apply a genetic algorithm (GA) for their optimization. We also propose a trajectory-fitting calibration theory based on the robotic arm’s special configuration and complete its movement accuracy calibration using a laser tracker. This calibration method reduces the robotic arm’s motion error by 71%, achieving an absolute positioning accuracy better than 3.5 mm, which meets the requirements for autonomous aerial refueling. In summary, this research presents a hybrid robotic arm that meets automatic docking requirements, offering a new approach to autonomous aerial refueling.

A Cognitive Stylistic Analysis of Oswald Mtshali’s “Men in Chains”

This paper presents a cognitive stylistic analysis of the poem “Men in Chains” by Oswald Mbuyiseni Mtshali (1968). Cognitive stylistics focuses primarily on explaining what happens during the reading process and how it influences the interpretations made by readers in understanding textual meaning. Though meaning is located in the formal structure of the literary text, readers can also approach meaning by deploying aspects of their previous background knowledge. Therefore, the study examines how a special configuration of language is used to realise a particular subject matter and a few selected figurative tropes to achieve a special aesthetic purpose. In the research, all primary data were sourced from the poem, and the secondary data were derived from related works and textbooks. The Schema Theory introduced by Richard C. Anderson (1977) and Conceptual Metaphor Theory by George Lakoff and Mark Johnson (1980) were used to analyse the data collected for the study. The analysis reveals that coherence among figures of speech and the use of extended and complex figurative expressions create new stylistic frameworks and metaphorical formulas that illustrate the idea of novelty and creativity in the poem. Based on the above, the work concludes that cognitive stylistics is effective in the study and interpretation of poetry. It can also be used in studying and teaching poetry to enhance better understanding and appreciation of any poetic texts.

A New Concept for Cervical Expansion Screws Using Shape Memory Alloy: A Feasibility Study.

In general, sufficient anchoring of screws in the bone material ensures the intended primary stability. Shape memory materials offer the option of using temperature-associated deformation energy in a targeted manner to compensate the special situation of osteoporotic bones or the potential lack of anchoring. An expansion screw was developed for these purposes. Using finite element analysis (FEA), the variability of screw configuration and actuator was assessed from shape memory. In particular, the dimensioning of the screw slot, the actuator length, and the actuator diameter as well as the angle of attack in relation to the intended force development were considered. As a result of the FEA, a special configuration of expansion screw and shape memory element could be found. Accordingly, with an optimal screw diameter of 4 mm, an actuator diameter of 0.8 mm, a screw slot of 7.8 mm in length, and an angle of attack of 25 degrees, the best compromise between individual components and high efficiency in favor of maximum strength can be predicted. Shape memory material offers the possibility of using completely new forms of power development. By skillfully modifying the mechanical and shape memory elements, their interaction results in a calculated development of force in favor of a high primary stability of the screw material used. Activation by means of body temperature is a very elegant way of initializing the intended locking and screw strength.

Unveiling the synergistic deformation mechanism in three-dimensional continuous network graphene-reinforced copper matrix composites

Three-dimensional continuous graphene-reinforced copper matrix composites (3D-Gr/Cu) have excellent mechanical properties due to their special configuration and strengthening mechanisms. To figure out the deformation mechanism of 3D-Gr/Cu, atomic models of 3D-Gr/Cu with different configuration of graphene network (GN) and concentration of defects are constructed and studied by molecular dynamic simulations in this work. Atomic structure evolution during stretching process is investigated, and the mechanical properties of 3D-Gr/Cu, GN and Cu models are studied. Thereby, synergistic deformation between GN and Cu in 3D-Gr/Cu is revealed, and the deformation coordination between GN and Cu is regulated by GN structure and Gr/Cu interface. Furthermore, intrinsic defects in graphene with a suitable concentration help to construct the three-dimensional GN, improve the Gr/Cu interface bonding, and thus enhance the deformation coordination between GN and Cu. These results provide a profound explanation of the deformation mechanism and a new basis for defect engineering in 3D-Gr/Cu.

Comparative analysis of performance of different types of Tesla valves

Abstract The Tesla valve is a valve with no moving parts. Its special configuration also allows the pressure drop of fluid passing through the Tesla valve from the reverse inlet to be much greater than the pressure drop of fluid passing through the Tesla valve from the forward inlet. Therefore, the Tesla valve has a certain degree of single pilot connectivity and pressure reduction capability. This article selected 8 common types of Tesla valves, By using numerical calculation method through Fluent simulation, the differences of resistance coefficient, pressure drop and Di value of 8 types of single-stage Tesla valves under three different working conditions were compared. In order to explore the performance of 8 kinds of Tesla valves under the three working conditions. At the same time, the relationship between the flow state and the structure of each part of the fluid before and after passing through the distributary pipe is analyzed. The simulation results show that due to the different structure of various Tesla valves, the flow rate, pressure and turbulent kinetic energy of the fluid inside the valve also have different changes. At the same time, there are many similarities in the flow state of some Tesla valves with similar structural types. Finally, according to the calculation results, the advantages and disadvantages of various types of Tesla valves and their applications are analyzed.

Macapá, a Brazilian equatorial magnetometer station: installation, data availability, and methods for temperature correction

Abstract. In the last 60 years, the largest displacement of the magnetic equator (by about 1100 km northwards) occurred in the Brazilian longitudinal sector. The magnetic equator passed by Tatuoca magnetic observatory (TTB) in northern Brazil in 2012 and continues to move northward. Due to the horizontal geomagnetic field geometry at the magnetic equator, enhanced electric currents in the ionosphere are produced – the so-called equatorial electrojet (EEJ). The magnetic effect of the EEJ is observed in the range of ±3° from the magnetic equator, where magnetic observatories record an amplified daily variation of the H component. In order to track the spatial and temporal variation of this phenomena, a new magnetometer station was installed in Macapá (MAA), which is about 350 km northwest of TTB. In this paper, we present the setup and data analysis of MAA station from November 2019 until September 2021. Because of its special configuration, we develop a method for temperature correction of the vector magnetometer data.

From single tube to multi-channel configuration: Constructing nanofiltration membranes with superior adhesion strength on the ceramic support by interfacial polymerization

Ceramic membranes with multi-channel configurations exhibit superior mechanical strength, high packing density, and high separation efficiency, making them more suitable for industrial applications than single tube and hollow fiber membranes. However, the preparation of thin-film composite (TFC) membranes on multi-channel ceramic supports remains a challenge because of the poor compatibility between organic and inorganic materials and their special configuration. In this study, nanofiltration membranes with a stabilized structure supported by 19-channel ceramic membranes were successfully prepared with improved adhesion strength and dynamic interfacial polymerization. TFC membranes with different channels exhibited uniform microstructure and stable performance. Specifically, polyethyleneimine, which served as a polymerization reactant, can be tightly wound on the surface of ceramic membranes via hydrogen bonding and van der Waals forces during interfacial polymerization. Owing to the enhanced cross-linked network interactions between the support and separation layers, the adhesion strength was significantly improved. Consequently, the optimized membranes maintained excellent rejection to MgCl2 of ∼94 % after the back-flush test under the pressure of 4 bar. Additionally, the multi-channel TFC membranes exhibited stable performance, with a pure water permeance of ∼14.9 LMH/bar and a molecular weight cut-off of 450 Da. When filtering sunset yellow for 10 h, the prepared membranes exhibited stable permeance and excellent rejection performance.

Pt Ternary Alloy with Rare Earth Metal - Pt5Ce-Co Intermetallic Nanoparticles as Highly Active Catalyst for Oxygen Reduction Reaction in PEMFCs

Alloying with rare earth elements is an important direction of exploring in Pt-based alloy design in oxygen reduction reaction (ORR), due to its special electronic configurations. Here we reported a ternary intermetallic alloy Pt5Ce-Co/C. The electronic valence state of Pt alloys was modified by the unique 4f orbital and the contraction effect of Ce, plus the additional active sites provided by the Co incorporation, optimizing the adsorption energy of the reaction intermediates, and effectively improving the electrocatalytic performance. The Pt5Ce-Co/C achieved 2.8 A∙mgPt -1 of mass activity in ORR catalysis, which can maintain 1.4 A∙mgPt -1 after 30k of accelerated stress test (AST). For membrane electrode assembly test, Pt5Ce-Co/C shows an initial mass activity of 724 mA∙mgPt -1, and a current density of 1876 mA∙cm-2 at 0.7 V under heavy-duty vehicle testing. This work provides an avenue to design advanced PGM ternary alloys by incorporating rare-earth metals to promote the catalyst performance.

Causal technological model for predicting void fraction and energy consumption in material extrusion process of polylactic acid

The rapid expansion of Additive Manufacturing (AM) technologies within the framework of Industry 4.0 raises important questions about their sustainability. Specifically, the widespread use of Material Extrusion (MEx) processes for polymeric materials necessitates a thorough evaluation of the possible balance between performance efficiency and sustainability, highlighting the need for further research into optimizing process parameters to ensure high product quality and reduced energy consumption. The present work focuses on Fused Filament Fabrication (FFF) technology, investigating the relationship between sustainability and print quality. A causal technology model is proposed to elucidate this relationship, underlining the impact of process parameters on defect generation and energy use. A real-time monitoring system for energy consumption measurements was employed to create an energy model that accounts for each component of the CreatBot F430 printer. An image analysis procedure was performed on a special configuration of the HIROX RH-2000 digital microscope to determine the void fraction of each sample. An experimental campaign was conducted, producing single-layer samples in polylactic acid (PLA) following a full factorial plan with four process parameters. A void fraction of up to 18 % was observed, with energy consumption per sample ranging between 638 J and 8843 J. Statistical analysis was performed to assess the impact of each process parameter and to determine operational ranges through the Response Surfaces Method. These results showed good agreement with the predicted conditions, demonstrating the model's effectiveness in supporting decision-making processes in technology selection.

Temperature impact on acoustic wave reflection in quasi-collinear acousto-optic devices.

Quasi-collinear geometry is a special configuration of acousto-optic (AO) diffraction that applies the acoustic wave reflection from the AO cell input optical face and provides an extremely large interaction length for achieving abnormally high spectral resolution of AO tunable filters. As a result, it becomes possible to implement the multifrequency diffraction which has found important applications for laser pulse shaping. The operation of quasi-collinear AO devices in the multifrequency diffraction regimen is accompanied by the appearance of the longitudinal and transverse temperature gradients in the crystal, mainly due to the acoustic power absorption. Temperature changes the AO cell material stiffness moduli, affecting the characteristics of the incident and reflected acoustic waves (propagation velocities and walk-off angles), and the reflection condition in general. On the example of paratellurite crystal is shown that the AO cell heating near the reflecting facet leads to a deviation of the reflected acoustic beam propagation direction from that specified during the AO cell manufacturing. The deviation magnitude depends on the reflection geometry choice and, in the paratellurite, may exceed several degrees, which adversely affects the AO diffraction characteristics, reducing the AO interaction efficiency and distorting the transmission function shape. The reflected beam deviation may be compensated by means of choosing the angle between the AO cell reflecting face and the piezoelectric transducer face, taking into account the operating AO device thermal regimen.

A mixed-dimensional formulation for the simulation of slender structures immersed in an incompressible flow

We consider the simulation of slender structures immersed in a three-dimensional (3D) flow. By exploiting the special geometric configuration of the slender structures, this particular problem can be modeled by mixed-dimensional coupled equations. Taking advantage of the slenderness of the structure and thus considering 3D/1D coupled problems raise several challenges and difficulties. From a mathematical point of view, these include defining well-posed trace operators of co-dimension two. On the computational standpoint, the non-standard mathematical formulation makes it difficult to ensure the accuracy of the solutions obtained with the mixed-dimensional discrete formulation as compared to a fully resolved one. Here we proposed to circumvent theses issues by imposing the fluid–structure coupling conditions on the 2D fluid–structure interface but in a reduced way still taking advantage of the 1D dynamic of the slender structure. We consider the Navier–Stokes equations for the fluid and a Timoshenko beam model for the structure. We complement these models with a mixed-dimensional version of the fluid–structure interface conditions, based on the projection of kinematic coupling conditions on a finite-dimensional Fourier space on each beam cross section. Furthermore, we develop a discrete fictitious domain formulation within the framework of the finite element method, establish the energy stability of the scheme, provide extensive numerical evidence of the accuracy of the discrete formulation, notably with respect to a fully resolved (ALE based) model and a standard reduced modeling approach.

Bioinspired Nanocomposite Dry Adhesives Applicable Over a Wide Temperature Range

AbstractBioinspired structural adhesives have shown great potential in the field of industrial manipulation and locomotion. However, such adhesives usually perform great adhesion performance at room temperature, reliable adhesion under high‐temperature conditions remains a major challenge and is rarely investigated, which severely limits the applications of current bioinspired adhesives. Here, a bioinspired adhesive structure based on fluororubber (FKM) and nanofillers is proposed. The adhesive structure has a “suction cup‐shaped” tip that mimics the special structural configuration of the adhesive setae of Dytiscus lapponicus, and the ability to regulate the structural modulus by adjusting the content of nanofillers, as well as exhibiting strong and contamination‐free adhesion (>350 kPa) with high adhesive efficiency (up to 77.7) in a wide temperature range (from room temperature to high temperatures (>200 °C)). Moreover, the adhesion performance can be enhanced by precisely regulating the structural modulus, and the enhancement mechanism is demonstrated based on the cohesive zone theory. The proposed adhesion strategy expands the application areas of dry adhesives from room‐ to high‐temperature conditions, especially for the pick‐up and transfer of thin and fragile materials that require high‐temperature operation, opening an avenue for the development of devices and systems based on dry adhesives.

Time Optimal Altitude-Hold Flight Mode Transition Strategy for a Class of Ducted Fan Tail Sitter UAV

For special tail sitter configurations such as the ducted fan tail sitter unmanned aerial vehicle (UAV), the widely used trajectory planning methodology based on differential flatness might not be applicable due to complex aerodynamic coupling effects. As a result, the flight mode transition remains a challenging task. In this paper, we address the time optimal altitude-hold flight mode transition issue for a class of ducted fan tail sitter UAV. The foundation of the framework is the dynamic transition corridor in which the limitation of jerk is particularly considered, aiming to thoroughly reflect the dynamic feature of aggressive maneuvers. Based on this, we propose a time optimal strategy to generate feasible altitude-hold transition trajectories. Simultaneous, by fully utilizing the manifestation of time optimal altitude-hold flight behavior revealed by the transition corridor, we try to tackle the time optimal altitude-hold transition by means of a novel model-free control scheme. Comparative simulations show that both of the transition strategies achieve satisfactory performance on time optimal altitude-hold transition in the absence of disturbance, while the model-free control scheme exhibits better robustness under external disturbance.

Massive twistor worldline in electromagnetic fields

We study the (ambi-)twistor model for spinning particles interacting via electromagnetic field, as a toy model for studying classical dynamics of gravitating bodies including effects of both spins to all orders. We compute the momentum kick and spin kick up to one-loop order and show precisely how they are encoded in the classical eikonal. The all-orders-in-spin effects are encoded as a dynamical implementation of the Newman-Janis shift, and we find that the expansion in both spins can be resummed to simple expressions in special kinematic configurations, at least up to one-loop order. We confirm that the classical eikonal can be understood as the generator of canonical transformations that map the in-states of a scattering process to the out-states. We also remark that cut contributions for converting worldline propagators from time-symmetric to retarded amount to the iterated action of the leading eikonal at one-loop order.

Tuning Iron Active Sites of FeOOH via Al3+ and Heteroatom Doping-Induced Asymmetric Oxygen Vacancy Electronic Structure for Efficient Alkaline Water Splitting.

Oxygen evolution reaction is the essential anodic reaction for water splitting. Designing tunable electronic structures to overcome its slow kinetics is an effective strategy. Herein, the molecular ammonium iron sulfate dodecahydrate is employed as the precursor to synthesize the C, N, S triatomic co-doped Fe(Al)OOH on Ni foam (C,N,S-Fe(Al)OOH-NF) with asymmetric electronic structure. Both in situ oxygen vacancies and their special electronic configuration enable the electron transfer between the d-p orbitals and get the increase of OER activity. Density functional theory calculation further indicates the effect of electronic structure on catalytic activity and stability at the oxygen vacancies. In alkaline solution, the catalyst C,N,S-Fe(Al)OOH-NF shows good catalytic activity and stability for water splitting. For OER, the overpotential of 10 mAcm-2 is 264mV, the tafel slope is 46.4 mVdec-1, the HER overpotential of 10 mAcm-2 is 188mV, the tafel slope is 59.3 mVdec-1. The stability of the catalyst can maintain ≈100h. This work has extraordinary implications for understanding the mechanistic relationship between electronic structure and catalytic activity for designing friendly metal (oxy)hydroxide catalysts.

Theoretical study of Jahn Teller effect on ion diffusion in trigonal prismatic 1H-MnO2

The trigonal prismatic MnO6 is a basic structural unit of Mn-based compound which has been seldom explored in theoretical and experimental studies. In this paper, the diffusion potential energy surfaces of Li+, Na+, K+, Mg2+, Al3+, and NH4+ ion in trigonal prismatic 1H-MnO2 are presented, which give an intuitionistic picture of the Jahn-Teller effect on the diffusion process. Based on the nudged elastic band method, it is found that the diffusion of ions in trigonal prismatic MnO2 is accompanied by consecutive structural distortion. The low diffusion barriers indicate trigonal prismatic MnO2 is a fast ion conductor. Band structure calculations show the semimetal characteristic of the intercalated compound, which is fit for high electrochemical performance. The details of the diffusion process are also highlighted. Our theoretical study provides an in-depth understanding and research on the fundamentals of the diffusion process in the special configuration in MnO2 and helps to develop novel high-performance electrodes.

Advanced computer architecture optimization for machine learning/deep learning

Abstract The recent progress in Machine Learning (Géron, 2022) and particularly Deep Learning (Goodfellow, 2016) models exposed the limitations of traditional computer architectures. Modern algorithms demonstrate highly increased computational demands and data requirements that most existing architectures cannot handle efficiently. These demands result in training speed, inference latency, and power consumption bottlenecks, which is why advanced methods of computer architecture optimization are required to enable the development of ML/DL-dedicated efficient hardware platforms (Engineers, 2019). The optimization of computer architecture for applications of ML/DL becomes critical, due to the tremendous demand for efficient execution of complex computations by Neural Networks (Goodfellow, 2016). This paper reviewed the numerous approaches and methods utilized to optimize computer architecture for ML/DL workloads. The following sections contain substantial discussion concerning the hardware-level optimizations, enhancements of traditional software frameworks and their unique versions, and innovative explorations of architectures. In particular, we discussed hardware including specialized accelerators, which can improve the performance and efficiency of a computation system using various techniques, specifically describing accelerators like CPUs (multicore) (Hennessy, 2017), GPUs (Hwu, 2015) and TPUs (Contributors, 2017), parallelism in multicore architectures, data movement in hardware systems, especially techniques such as caching and sparsity, compression, and quantization, other special techniques and configurations, such as using specialized data formats, and measurement sparsity. Moreover, this paper provided a comprehensive analysis of current trends in software frameworks, Data Movement optimization strategies (A.Bienz, 2021), sparsity, quantization and compression methods, using ML for architecture exploration, and, DVFS (Hennessy, 2017),, which provides strategies for maximizing hardware utilization and power consumption during training, machine learning, dynamic voltage, and frequency scaling, runtime systems. Finally, the paper discussed research opportunity directions and the possibilities of computer architecture optimization influence in various industrial and academic areas of ML/DL technologies. The objective of implementing these optimization techniques is to largely minimize the current gap between the computational needs of ML/DL algorithms and the current hardware’s capability. This will lead to significant improvements in training times, enable real-time inference for various applications, and ultimately unlock the full potential of cutting-edge machine learning algorithms.