High-performance Design Research Articles

Synergistic Electronic Interaction in PdCu Alloy/TiO2-NSs for Ambient Efficient Dehydrogenation of Formic Acid.

Palladium-based catalysts are remarkable in endorsing hydrogen (H2) generation through formic acid (HCOOH, FA) dehydrogenation under near-ambient conditions. Hydrogen energy efficiency depends on high-performance catalyst design. In this study, Pd-Cu nanoalloy catalysts with mutable atomic ratios are successfully fabricated on TiO2 nanosheets (TiO2-NSs).The synergistic electronic interactions between Palladium (Pd) and copper (Cu) are revealed through a density of states (DOS) analysis of alloy supported over a mixed valence state of Ti linked to oxygen vacancies (Vo) in TiO2-NSs, Enhanced adsorption of target molecules reveals novel active sites for Formic acid dehydrogenation (FAD), with O─H bond cleavage via HCOO formate intermediate preceding C─H and C─O bond cleavage. Experimental and theoretical research demonstrates that the Pd-Cu/TiO2-NSs (3:7) catalyst d electron redistribution and d-band center shift lower the activation energy for O─H bond cleavage, exhibit superior H2 production than pure Pd and Cu, increasing Pd electron density due to synergistic effects from reactive crystal facets, defects, and strong metal support interactions (SMSI), lowering the activation energy for HCOOH dissociation step, generating carbon dioxide (CO2) and H2 with a unprecedented high turnover frequency (TOF) of 6268 molH2.h-1.molPd-1 at 303K with activation energy (Ea) of 15 KJ mol-1. This attempt models an efficient HCOOH-to-hydrogen catalyst.

DESIGN OF A 24.5 GHZ CMOS LOW NOISE AMPLIFIER USING 0.13-µM TECHNOLOGY FOR 6G WIRELESS APPLICATIONS

The need for high-performance circuit designs is growing as wireless communication technologies continue to advance and support newer generations of wireless applications. Much emphasis has been focused on the possibility of the 24 GHz frequency band in next-generation wireless networks, including 5G and beyond. Designing a low noise amplifier (LNA) operating at 24 GHz presents several challenges. The primary concerns include achieving high gain, low power consumption, low noise figure, and while maintaining good linearity and stability. This paper presents the design, simulation, and layout of a CMOS LNA optimized for operation at 24.5 GHz frequency, targeting 6G and beyond wireless communication applications. The proposed LNA employs three stages with a cascode topology at the first stage and follow by a common source stage at second and third stage. The three stages help to achieve high gain, and the source degeneration inductor at the first stage helps to improve linearity. Extensive simulations were conducted using a 0.13-µm CMOS technology, demonstrating a peak gain of 21 dB and a noise figure of 5.6 dB at 24.5 GHz. The LNA also exhibits good linearity and stability over a wide bandwidth. The performance metrics were validated through simulation and comparison, showcasing the feasibility of the designed LNA for 6G applications. This work contributes to the advancement of CMOS-based radio frequency (RF) front-end designs for next-generation wireless communication systems.

Hierarchical meta-porous materials as sound absorbers

The absorption of sound has great significance in many scientific and engineering applications, from room acoustics to noise mitigation. In this context, porous materials have emerged as a viable solution towards high absorption performance and lightweight designs. However, their performance is somewhat limited in the low-frequency regime. Inspired by the concept of recursive patterns over multiple length scales, typical of many natural materials, here, we propose a hierarchical organization of multilayered porous media and investigate their performance in terms of sound absorption. Two types of designs are considered: a hierarchical periodic (HP) and a hierarchical gradient (HG). In both cases, it is found that in some frequency ranges the introduction of multiple levels of hierarchy simultaneously allows for: (i) an increase in the level of absorption compared with the corresponding bulk block of porous material (or to the previous hierarchical levels (HLs)), and (ii) a reduction in the quantity of porous material required. Another advantage of using this approach is on the fabrication, since only one porous material is required. The performances are examined for both normal and oblique incidences, as well as for different values of the static air-flow resistivity. The methodological approach is based on the transfer-matrix method, optimization algorithms such as the metaheuristic greedy randomized adaptive search procedure (GRASP), finite-element calculations and measurements performed in an impedance tube.

From biomass to power: High-performance reactor design for coking-resistant operation

Biomass gasification coupled with solid oxide fuel cell (SOFC) technology utilizes the gas generated from biomass gasification directly as fuel for SOFC, thereby realizing power generation from solid waste. This technology combines the carbon–neutral feature of biomass with the high efficiency and low emissions of SOFC, making it a promising route for clean energy generation. However, biomass gasification syngas possesses a complex composition, including a high concentration of inert gases, which imposes higher requirements on SOFC. This study developed a multi-channel, hierarchical structural design based on the commercial NiO-yttria-stabilized zirconia (YSZ) material system, realizing high-performance power generation using biomass gasification syngas. The results showed that the combination of a unique structural design and an enhanced interface electrochemical reaction effectively mitigates the influence from inert composition dilution. When operating in gasification syngas with nearly 60 % inert components, the power density can reach 2.07 W·cm−2 (750 °C). In addition, due to the spatial separation of the inert support region and the electrochemically active region, the effect of controlling the position of carbon deposits was achieved, demonstrating 100 h stable operation with dry biomass gasification syngas. Hence, the combination of micro-tubular SOFC with distinctive structural regulation and biomass gasification exhibits promising prospects for further development.

Enhancing strength and ductility synergy through heterogeneous laminated structure design in high-entropy alloys

Heterogeneous laminated structure (HLS) design offers new opportunities to enhance the mechanical performance of high-entropy alloys (HEAs) through synergistic effects from heterogeneity. However, it remains challenging to introduce the HLS into HEAs via severe plastic deformation due to their strong work-hardening capacity. In this study, a specially designed multi-level HLS, characterized by alternatively stacked micro-grained soft CoCrFeNi layers and nanostructured ultra-hard Al0.3CoCrFeNi layers containing a three-phase microstructure (composed of nanograined face-centered cubic matrix, (Al, Ni)-rich B2 precipitates, and Cr-rich σ precipitates), is controllably introduced into FCC HEAs via a conventional thermo–mechanical processing involving hot-pressing, cold-rolling, and annealing. Meanwhile, thermo–mechanical processing induces Al element diffusion across the layer interface, resulting in the formation of an interfacial transition layer and the establishment of a strong interface bonding between the neighboring CoCrFeNi and Al0.3CoCrFeNi layers. As a result, the multi-level HLSed CoCrFeNi/Al0.3CoCrFeNi composite exhibits a yield strength as high as 1127±25.4 MPa while maintaining a large fracture elongation (up to (26.3±2.4)%). Such an excellent strength–ductility synergy surpasses that of most previously reported high-performance monolithic bulk CoCrFeNi and Al0.3CoCrFeNi HEAs prepared through careful chemical composition optimization and/or thermo–mechanical processing. Strong hetero-deformation induced strengthening benefited from the apparent microstructural/microhardness difference and the strong interface bonding between the neighbouring CoCrFeNi and Al0.3CoCrFeNi layers, together with simultaneous activation of multiple strain hardening mechanisms containing mechanical twinning, stacking faults and precipitation strengthening, is responsible for the excellent strength–ductility combination. This multi-level HLS and its fabrication strategy provide an enlightening way to develop strong and ductile HEAs and can also be applied to high-performance designs of other metallic materials.

Optimization of High-performance Materials Design and Manufacturing Processes in Mechanical Engineering

Optimization of High-performance Materials Design and Manufacturing Processes in Mechanical Engineering

Rheological investigation of heavy oil-in-water pickering emulsions for potential applications of fracturing fluids

Pickering emulsions as a kind of emulsified fracturing fluids can enhance the efficacy of shale fracturing. Investigation of the rheological properties of Pickering emulsions yields valuable insights for their in-situ fracturing applications. In this work, nano-clay particles and heavy crude oil were employed to form oil-in-water (O/W) Pickering emulsions. The impact of salinity, pH, and temperature on the rheological properties, including viscosity property and viscoelastic property, of Pickering emulsions were tested and analyzed. The results showed that the viscosity of heavy O/W Pickering emulsions were significantly affected by the change of salinity and pH values, with higher salinity or lower pH resulting in increased viscosity. Under ultra-high salinities (>5 wt% NaCl), the coalescence stability of Pickering emulsion strengthens and creaming stability declines. The massive formation of clay clusters leads to a reduction in the number of nano-clay particles used to generate the network, causing instability in the emulsion creaming. The heavy O/W Pickering emulsions exhibited high stability and high viscosity characteristics, which were mainly caused by the change of surface charge of nano-clay particles and the corresponding change of particle adsorption on droplet surface and particle-particle interactions. At a salinity of 2.5% NaCl and a pH of 4.6, the emulsion system remained stable at a temperature up to 120 °C, holding a viscosity reaching 74.1 mPa s at a shear rate of 6.81 s−1. Besides, heavy O/W Pickering emulsions were viscoelastic fluids, with both noticeable viscous property and elastic property at the same time. Elastic modulus (G′) and viscous modulus (G″) of the emulsion increased with increasing salinity and decreasing pH, enhancing great deformation capacity. Remarkably, the Pickering emulsions could maintain its viscoelastic behavior at varying temperature conditions. For the first time, rheological study of Pickering emulsions formed by nano-clay nanoparticles as a single emulsifier to heavy oil. Our study underscores the emulsifying ability of nano-clay particles to heavy crude oils, under a wide range of salinity and pH, yielding Pickering emulsions with exceptional stability and rheological properties. This novel emulsion system, possessing high resistance to temperature and salinity, holds promise for a high-performance fracturing fluid design.

Design of Low Power Control Unit for RISC-V Processor Core

This research work focuses on the development of a low-power decode logic for a RISC-V processor core with specifications. The goal is to create a controller that performs all six groups of instruction formats outlined in the RV32I Base Integer Instruction Set. The control unit is designed to decode a total of 13 instruction sets, allowing for a comprehensive range of operations. A single instruction pipeline approach is implemented in the design to optimize performance. The synthesis of the design is carried out using the 32 nm standard library, resulting in a maximum operating frequency of 666.67 MHz. To further enhance power efficiency, clock gating techniques are employed, leading to a reduction in power consumption by 18.72 % from 112.15 µW to 91.45 µW. Additionally, the layout of the design is optimized, resulting in an area of 354.74 mm2. The successful development of this low-power decode logic demonstrates its potential for integration into larger RISC-V processor cores. Future enhancements can include expanding the instruction decoding capability to encompass the full range of 47 instructions in the RV32I Base Integer Instruction Set, as well as exploring additional pipeline stages to further improve performance. The results achieved in this project contribute to the ongoing pursuit of power-efficient and high-performance processor designs based on the RISC-V architecture.

Design of energy-efficient full subtractor circuit at near threshold computing for signal processing application

Abstract Energy Efficiency is a critical factor while designing integrated circuits. Therefore, a 1-bit full subtractor (FS) cell is proposed for lower power application by employing Gate Level Body Biasing (GLBB) scheme for Near Threshold Computing (NTC) application to conquer a unique module for achieving full swing borrow output.We evaluate power, delay, energy and the product of energy with delay (EDP) metrics with respect to C-CMOS full subtractor. The proposed feedback based with FS with GLBB technique has a total die area of 60.02 μm2, while the average power, delay, and energy are 1138 pW, 242 ns, and 27.53 aJ, respectively. The results revealed that our proposed subthreshold hybrid FS circuit with GLBB scheme successfully achieved more than 10.46% average power consumption, 26.58% energy consumption reductions, and 17.98% EDP savings compared to conventional CMOS configuration and other hybrid counterparts. GLBB circuits with FS achieve performance levels that are not affordable in C-CMOS, DTMOS,and GLBB with full adder configurations. Therefore, the FS circuit serves as an efficient divider circuit in terms of detecting objects for image processing applications.

Machine-learning synergy in high-entropy alloys: A review

High-entropy alloys (HEAs) have attracted significant attention because of their exceptional mechanical properties and potential for discovering new compositions. However, owing to their complex chemical makeup, understanding the underlying physical mechanisms and designing novel alloys through traditional theoretical and computational methods are challenging. Machine learning (ML) offers a promising solution to address these challenges. This review begins with a discussion of the critical data preprocessing techniques required for effective ML applications in the HEA domain. Subsequently, this review discusses various ML models, including supervised and unsupervised algorithms, focusing on guiding readers through algorithm selection and model evaluation. These fundamental aspects are essential for understanding the complexities of applying ML in HEA research. Further, this review highlights various successful applications of ML in HEA research. These include optimisation of the alloy composition, processing parameters, and microstructural characteristics to enhance the mechanical properties. In addition, this review examines the use of ML for performance-driven reverse engineering of HEA compositions, enabling the rapid identification of new high-performance alloy designs. The novelty of this review is its comprehensive and integrative approach to ML applications in HEAs. Unlike previous studies that focused on specific ML techniques or isolated use cases, this review explores the transformative potential of ML across the entire HEA research landscape. By covering the full spectrum from fundamental data preprocessing and model selection to a diverse range of practical applications, this review provides insights into how ML can accelerate the discovery and development of high-performance HEAs. This multifaceted perspective covers the synergistic interplay between various ML methodologies and their impact on HEA research, opening up new opportunities for innovation that may not have been fully explored in more specialized studies. Considering the extensive studies on ML discussed in this review, it can be concluded that ML has revolutionised the design and development of HEAs. By optimising alloy composition, processing parameters, and microstructural characteristics, ML-driven approaches have unlocked the potential for engineering high-performance HEAs with tailored mechanical properties. Moreover, the use of ML in performance-driven reverse engineering has enabled the rapid identification of promising HEA compositions, thereby accelerating the discovery of novel materials.

High-Performance Latch Designs of Double-Node-Upset Self-Recovery and Triple-Node-Upset Tolerance for Aerospace Applications

High-Performance Latch Designs of Double-Node-Upset Self-Recovery and Triple-Node-Upset Tolerance for Aerospace Applications

Robust topology optimization of multi-material structures with overhang angle constraints using the material field series-expansion method

This paper presents a novel topology optimization method to enhance the manufacturability and practicality of multi-material structures. Traditional topology optimization techniques excel at generating high-performance designs, but the performance of these designs is often compromised due to the load perturbations encountered in real-world applications or post-processing for additive manufacturing (AM). This work addresses this challenge by incorporating three key features directly into the optimization process. First, the method integrates overhang angle constraints, ensuring designs are inherently manufacturable with AM. Second, it utilizes the material field series-expansion method for a significant reduction in design variables while simultaneously providing a clear and efficient description of material distribution within the optimized designs. Finally, to enhance design robustness under real-world scenarios with uncertain loads, the method employs Monte Carlo simulation, which is decoupled from the optimization process to reduce the computational cost. Numerical examples demonstrate the efficiency and effectiveness of the proposed method in generating robust designs of multi-material structures well-suited for AM. The proposed framework offers a promising pathway for the realization of complex, high-performance composite structures.

Applications of machine learning method in high-performance materials design: a review

Applications of machine learning method in high-performance materials design: a review

Motion-free high-resolution on-chip microscopy using LED matrix

Lensless microscopy is an imaging technique that allows high-resolution imaging over a large field of view with a cost-effective design. Conventional lensless microscopy often utilizes multi-height phase retrieval and pixel-super-resolution algorithms to reconstruct high-resolution images, requiring mechanical stages for three-dimensional relative movements between a light source, camera, and sample. However, the excessive use of stages inevitably increases the bulkiness of the system and extends the image acquisition time. Here, we propose a motion-free lensless microscope that incorporates an RGB LED matrix array. A high-resolution holographic image is reconstructed from subpixel-shifted color images obtained with LED illuminations without any mechanical movement. Using a prototype system, we have demonstrated a spatial-bandwidth product of 30 megapixels with a resolution of 0.87 µm and a field of view of 24 mm2. The usability of the proposed method has been further tested for histopathologic examination. Our system features a compact and high-performance design with inexpensive optoelectronic elements, a conventional CMOS sensor and an LED matrix, which are well-aligned with the original design motivation of lensless imaging methods.

Representation Learning for Sequential Volumetric Design Tasks

Abstract Volumetric design, also called massing design, is the first and critical step in professional building design which is sequential in nature. As the volumetric design process is complex, the underlying sequential design process en- codes valuable information for designers. Many efforts have been made to automatically generate reasonable volumetric designs, but the quality of the generated design solutions varies, and evaluating a design solution requires either a prohibitively comprehensive set of metrics or expensive human expertise. While previous approaches focused on learning only the final design instead of sequential design tasks, we propose to encode the design knowledge from a collection of expert or high-performing design sequences and extract useful representations using transformer-based models. Later we propose to utilize the learned representations for crucial downstream applications such as design preference evaluation and procedural design generation. We develop the preference model by estimating the density of the learned representations whereas we train an autoregressive transformer model for sequential design generation. We demonstrate our ideas by leveraging a novel dataset of thousands of sequential volumetric designs. Our preference model can compare two arbitrarily given design sequences and is almost 90% accurate in evaluation against random design sequences. Our autoregressive model is also capable of autocompleting a volumetric design sequence from a partial design sequence.

Carbon-mechanic dual-control design decision model of prefabricated structural components

Prefabricated enclosure structures (PES) have become the key technology for low-carbon construction in the engineering field due to their low labor demand, fast construction speed, and low environmental pollution. In the context of low-carbon development, efficiently designing PES that not only possess high-strength but also demonstrate significant greenhouse gas (GHG) reduction benefits has become a critical challenge. The objective of this study is to address the challenges of automated design and scientific decision-making methods for PES components. First, the Carbon-mechanic dual-control assessment model (CDAM) is proposed, providing a novel evaluation dimension for the design of prefabricated components. Subsequently, based on the CDAM, an automated design decision-making process, and model for components are established using an improved genetic algorithm and parameterized method. Finally, under the constraints of actual engineering conditions, adaptive solutions were automatically generated for specific cases. These solutions were comprehensively evaluated and optimized using CDAM, resulting in design guidelines and optimal solutions for PES that balance environmental performance and load-bearing capacity. The case analysis results show that the optimal scheme based on the carbon-mechanic dual-control (CM) index is: the sectional dimension is 830 mm, the reinforcement ratio is 0.9 % and the cavity ratio is 45 %, which shows the ideal characteristics of low-carbon and high-strength. Compared with the single index decision scheme, this scheme achieve a maximum of 42 % GHG emissions reduction. The high comprehensive performance scheme is characterized by small sectional dimension, high reinforcement ratio, and high cavity ratio. Cavity ratio is a key parameter to achieve high performance design. The research findings provide an important theoretical basis and technical support for the low-carbon transformation in the engineering field.

Heterogeneous Interface Design with Oxygen Vacancy-Rich Assistance High-Capacity Titanium-Based Oxide Anode Materials for Sodium-Ion Batteries.

Researchers are paying more attention to sodium-ion batteries (SIBs) because of their abundant supply of sodium resources and affordable price. TiO2 offers excellent safety and a long lifespan as an anode material for SIBs. However, the process kinetics is slow due to its limited Na+ storage efficiency, weak conductivity, and irreversible Na+ capture. In order to address these issues, this review uses a mix of the template approach and the double-hydrolysis method to manage the structure and diffusion of TiO2-based anode materials by synthesizing FeTiO3/TiO2 heterostructured double-shell microspheres (FTO). Through the built-in electric field effect caused by their heterostructures, FTO materials improve reaction kinetics, boost electronic conductivity, and lower the diffusion energy barrier of Na+. Their distinctive double-shell structure can increase electrolyte infiltration, shorten the diffusion distance between ions and electrons, and accommodate volume expansion during cycling. Furthermore, the irreversible capture of Na+ and the unfavorable interactions between the surface active site and electrolyte can be successfully inhibited by FTO heterostructures. FTO has an exceptionally high capacity (reaching 362.7 mA h g-1 after 60 cycles at 20 mA g-1) and excellent cycle stability (with a decay rate of 0.0061% after 1000 cycles at 2 A g-1). The strategy of constructing heterogeneous interfaces assists with high-performance SIB anode design.

Challenges and Prospects of Electrolyte Design for Lithium‐Sulfurized Polyacrylonitrile Batteries

Sulfurized polyacrylonitrile (SPAN) is regarded as a promising organic sulphur cathode material for lithium‐sulfur (Li‐S) batteries. It undergoes a solid‐solid conversion without forming polysulfide intermediate phases, overcoming the poor electrochemical performance caused by the shuttle effect of elemental S cathodes. However, the realization of this unique conversion mechanism requires to employ appropriate electrolytes. Furthermore, the direct application of metallic Li as the anode unavoidable introduces a series of issues triggered by Li dendrites in Li‐SPAN batteries, such as low lifespan, short circuits, fire, etc. In this review, we endeavor to encapsulate recent advancements in electrolyte research, with a particular focus on the intrinsic relationship between the solvation structure of the electrolyte and the interfacial chemistry of the Li anode and SPAN electrode, aim to provide insights into the electrolytes design for high performance Li‐SPAN full batteries.

The effect of targeting both quantitative and qualitative objectives in generative design tools on the design outcomes

Current generative design tools backed by artificial intelligence (AI) primarily allow for quantitative inputs while qualitative aspects of a design, in particular aesthetics, have been shown to be considered indirectly by designers. To explore this further, controlled lab experiments were conducted to understand how designers incorporate quantitative and qualitative objectives while using generative design tools and how their behavior may affect design performance. Thirty-four participants completed a design task with quantitative and qualitative objectives with and without generative design tools. The outcomes produced using generative design tools displayed a greater aesthetic diversity and expanded a larger portion of the objective space compared to those without using a generative design tool. Participants also expressed the ability to focus on the qualitative objectives by delegating the quantitative objective to the generative design tool. This showcases the potential for high-performing generative design tools to assist human designers by alleviating part of their cognitive load when balancing multiple objectives, giving them the bandwidth to focus on other objectives not fully incorporated by the tool. In this way, leveraging the expertise of both the human designer and the generative design tool can allow for greater consideration of various objectives throughout the design process.

Unraveling Energy Storage Performance and Mechanism of Metal-Organic Framework-Derived Copper Vanadium Oxides with Tunable Composition for Aqueous Zinc-Ion Batteries.

Achieving high-performance aqueous zinc (Zn)-ion batteries (AZIBs) requires stable and efficient cathode materials capable of reversible Zn-ion intercalation. Although layered vanadium oxides possess high Zn-ion storage capacity, their sluggish kinetics and poor conductivity present significant hurdles for further enhancing the performance of AZIBs. In response to this challenge, a dissolution-regrowth and conversion approach is formulated using metal-organic frameworks (MOFs) as a sacrificial template, which enables the in situ creation of copper vanadium oxides (CuVOx) with porous 1D channels and distinctive nanoarchitectures. Owing to their distinctive structure, the optimized CuVOx cathode experiences a reaction involving the synergistic insertion/extraction of Zn2+, resulting in rapid Zn2+ diffusion kinetics and enhanced electrochemical activity postactivation. Specifically, the activated electrode delivers a reversible capacity of 519mAhg-1 at 0.5Ag-1 for AZIBs. It is noteworthy that the electrode exhibits a remarkable reversible rate capacity of 220mAhg-1 at 5Ag-1 with excellent durable cycleability, retaining 88% of its capacity even after 3000 cycles. Various ex situ testing methods endorse the reversible insertion/extraction of Zn2+ in the CuVOx cathode. This study provides a novel insight into high-performance MOF-derived unique structure designs for AZIB electrodes.