A ribbed strategy disrupts conventional metamaterial deformation mechanisms for superior energy absorption

\ua9 2024 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.Enhancing energy absorption in mechanical metamaterials has been a focal point in structural design. Traditional methods often include introducing heterogeneity across unit cells. Herein, we propose a straightforward ribbed strategy to achieve exceptional energy absorption. We demonstrate our concept through modified body-centered cubic (BCC) and face-centered cubic (FCC) ribbed truss-lattice metamaterials (BCCR and FCCR). Using stainless-steel 316L samples, compression tests indicate a 111% and 91% increase in specific energy absorption (SEA) for BCCR and FCCR, respectively, along with an enhancement in compression strength by 61.8% and 40.7%. Deformation mechanisms are comprehensively elucidated through both finite element analysis and theoretical calculations. The mitigation of stress concentration at nodes, redistribution of load transfer pathways within struts, and introduction of multiple plastic hinges collectively contribute to increased energy absorption and higher compression strength. Using rein-based polymer samples, the ribbed truss-lattice metamaterials also exhibit exceptional damage tolerance, experiencing only a 15% loss in maximum strength after cyclic compression at 20% strain, while maintaining a 73% higher SEA compared to their non-ribbed counterpart. This strategy extends beyond the discussed structures, presenting itself as a generic approach to enhance plateau strength and SEA

Effects of Three Gorges Reservoir (TGR) water storage in June 2003 on Yangtze River sediment entering the estuary

International audienceThe world-greatest water conservancy project, Three Gorges Reservoir (TGR), stored water for the first time in June 2003, which provides an excellent opportunity to examine its effects on the sediment entering the Yangtze River estuary. A daily record dataset of water discharge and suspended sediment concentration (SSC) of the Yangtze River measured at Datong (the controlling hydrological gauging station into the estuary) from May 15 to July of 2003 spanning the water storage, together with a monthly record dataset of runoff, sediment load and SSC measured at Datong from 1953 to 2003, were used to examine the effects of the TGR water storage in June 2003 on the Yangtze River sediment entering the estuary. The results show that the unnaturally clearer water due to the TGR sedimentation resulted by the water storage in June 2003 brought the Yangtze River markedly decreased SSC and sediment load entering the estuary both during the TGR water storage and in the second half year of 2003. The Yangtze River water and sediment discharges into the estuary from 15 May to 15 July in 2003 spanning the TGR water storage clearly indicated three phases: (1) pre-water storage of the TGR from 15 May to 25 May, during this phase, SSC and sediment load increased with water discharge increasing; (2) water storage of the TGR from 25 May to 10 June (including the preparation phase from 25 May to 31 May), during this phase, SSC and sediment load decreased dramatically with water discharge decreasing; and (3) post-water storage of the TGR, at the beginning, SSC, sediment load and water discharge basically remained at a relatively low value until the end of June, and since then, SSC and sediment load increased gradually with water discharge increasing. In addition, the real total sediment load was reduced by 2456.07×104 t than the estimated total sediment load during the period from 27 May to 2 July in 2003

Drought events and their effects on vegetation productivity in China

Many parts of the world have experienced frequent and severe droughts during the last few decades. Most previous studies examined the effects of specific drought events on vegetation productivity. In this study, we characterized the drought events in China from 1982 to 2012 and assessed their effects on vegetation productivity inferred from satellite data. We first assessed the occurrence, spatial extent, frequency, and severity of drought using the Palmer Drought Severity Index (PDSI). We then examined the impacts of droughts on China\u27s terrestrial ecosystems using the Normalized Difference Vegetation Index (NDVI). During the period 1982–2012, China\u27s land area (%) experiencing drought showed an insignificant trend. However, the drought conditions had been more severe over most regions in northern parts of China since the end of the 1990s, indicating that droughts hit these regions more frequently due to the drier climate. The severe droughts substantially reduced annual and seasonal NDVI. The magnitude and direction of the detrended NDVI under drought stress varied with season and vegetation type. The inconsistency between the regional means of PDSI and detrended NDVI could be attributed to different responses of vegetation to drought and the timing, duration, severity, and lag effects of droughts. The negative effects of droughts on vegetation productivity were partly offset by the enhancement of plant growth resulting from factors such as lower cloudiness, warming climate, and human activities (e.g., afforestation, improved agricultural management practices)

Robust Quantum State Transfer in Random Unpolarized Spin Chains

We propose and analyze a new approach for quantum state transfer between remote spin qubits. Specifically, we demonstrate that coherent quantum coupling between remote qubits can be achieved via certain classes of random, unpolarized (infinite temperature) spin chains. Our method is robust to coupling strength disorder and does not require manipulation or control over individual spins. In principle, it can be used to attain perfect state transfer over arbitrarily long range via purely Hamiltonian evolution and may be particularly applicable in a solid-state quantum information processor. As an example, we demonstrate that it can be used to attain strong coherent coupling between Nitrogen-Vacancy centers separated by micrometer distances at room temperature. Realistic imperfections and decoherence effects are analyzed.Comment: 4 pages, 2 figures. V2: Modified discussion of disorder, added references - final version as published in Phys. Rev. Let

Nanowires Framework Supported Porous Lotus-Carbon Anode Boosts Lithium-Ion and Sodium-Ion Batteries

The novel design of carbon materials with stable nanoarchitecture and optimized electrical properties featuring simultaneous intercalation of lithium ions (Li+) and sodium ions (Na+) is of great significance for the superb lithium–sodium storage capacities. Biomass-derived carbon materials with affluent porosity have been widely studied as anodes for lithium-ion batteries (LIBs) and sodium-ion batteries (SIBs). However, it remains unexplored to further enhance the stability and utilization of the porous carbon skeleton during cycles. Here, a lotus stems derived porous carbon (LPC) with graphene quantum dots (GQDs) and intrinsic carbon nanowires framework (CNF) is successfully fabricated by a self-template method. The LPC anodes show remarkable Li+ and Na+ storage performance with ultrahigh capacity (738 mA h g−1 for LIBs and 460 mA h g−1 for SIBs at 0.2 C after 300 cycles, 1C≈372 mA h g−1) and excellent long-term stability. Structural analysis indicates that the CNFs-supported porous structure and internal GQDs with excellent electrical conductivity contribute significantly to the dominant capacitive storage mechanism in LPC. This work provides new perspectives for developing advanced carbon-based materials for multifunctional batteries with improved stability and utilization of porous carbon frameworks during cycles

Performance of multiple neural networks in predicting lower limb joint moments using wearable sensors

Joint moment measurements represent an objective biomechemical parameter in joint health assessment. Inverse dynamics based on 3D motion capture data is the current 'gold standard’ to estimate joint moments. Recently, machine learning combined with data measured by wearable technologies such electromyography (EMG), inertial measurement units (IMU), and electrogoniometers (GON) has been used to enable fast, easy, and low-cost measurements of joint moments. This study investigates the ability of various deep neural networks to predict lower limb joint moments merely from IMU sensors. The performance of five different deep neural networks (InceptionTimePlus, eXplainable convolutional neural network (XCM), XCMplus, Recurrent neural network (RNNplus), and Time Series Transformer (TSTPlus)) were tested to predict hip, knee, ankle, and subtalar moments using acceleration and gyroscope measurements of four IMU sensors at the trunk, thigh, shank, and foot. Multiple locomotion modes were considered including level-ground walking, treadmill walking, stair ascent, stair descent, ramp ascent, and ramp descent. We show that XCM can accurately predict lower limb joint moments using data of only four IMUs with RMSE of 0.046 ± 0.013 Nm/kg compared to 0.064 ± 0.003 Nm/kg on average for the other architectures. We found that hip, knee, and ankle joint moments predictions had a comparable RMSE with an average of 0.069 Nm/kg, while subtalar joint moments had the lowest RMSE of 0.033 Nm/kg. The real-time feedback that can be derived from the proposed method can be highly valuable for sports scientists and physiotherapists to gain insights into biomechanics, technique, and form to develop personalized training and rehabilitation programs

Enhancing Received Signal Strength-Based Localization through Coverage Hole Detection and Recovery

In wireless sensor networks (WSNs), Radio Signal Strength Indicator (RSSI)-based localization techniques have been widely used in various applications, such as intrusion detection, battlefield surveillance, and animal monitoring. One fundamental performance measure in those applications is the sensing coverage of WSNs. Insufficient coverage will significantly reduce the effectiveness of the applications. However, most existing studies on coverage assume that the sensing range of a sensor node is a disk, and the disk coverage model is too simplistic for many localization techniques. Moreover, there are some localization techniques of WSNs whose coverage model is non-disk, such as RSSI-based localization techniques. In this paper, we focus on detecting and recovering coverage holes of WSNs to enhance RSSI-based localization techniques whose coverage model is an ellipse. We propose an algorithm inspired by Voronoi tessellation and Delaunay triangulation to detect and recover coverage holes. Simulation results show that our algorithm can recover all holes and can reach any set coverage rate, up to 100% coverage

A quantitative research on S- and SO2-poisoning Pt/Vulcan carbon fuel cell catalyst

A quantitative research on S and SO2 poisoning Pt/Vulcan carbon (Pt/VC) catalysts for fuel cells was conducted by the three-electrode method. Pt/VC electrodes were contaminated by submersion in a SO2-containing solution made up of 0.2 mM Na2SO3 and 0.5 M H2SO4 for different periods of time, and held at 0.05 V (vs. RHE) in 0.5 M H2SO4 solutions in order to gain zero-valence sulfur (S-0) poisoned electrodes. The sulfur coverage of Pt was determined from the total charge consumed as the sulfur was oxidized from S-0 at 0.05 V (vs. RHE) to sulfate at >1.1 V (vs. RHE). The summation of initial coverage of S-0 (theta(S)) and coverage of H (theta(H)) are approximately equal to 1 (theta(H) + theta(S) = 1) when 0.5 < theta(H) < 1, which gives an easy way to figure out the quantity of sulfur adsorbed on Pt/VC. As to SO2 poisoned Pt/VC, the catalytic activity of oxygen reduction reaction (ORR) decreases linearly with the amount of SO2 adsorbed on the Pt nanoparticles when 0.45 < theta(H) < 0.81. When the adsorbed SO2 was reduced to S-0 the mass activity was greatly recovered, and the area specific activity was even higher than the unpoisoned Pt/VC. (C) 2012 Elsevier Ltd. All rights reserved