Ultrafast synthesis of efficient TS-PtCoCu/CNTs composite with high feed-to-product conversion rate by Joule heating for electrocatalytic oxidation of ethanol.

Due to the challenges involved in achieving high metal load, uniform metal dispersion and nanosized metal particles simultaneously, it is difficult to develop a simple protocol for the rapid and efficient synthesis of Pt-based composites for electrocatalytic ethanol oxidation reaction (EOR). In this study, a facile ultrafast thermal shock strategy via Joule heating was applied to fabricate a series of PtCoCu ternary nanoalloys decorated carbon nanotube composites (TS-PtCoCu/CNTs), without the need for a reducing agent or surfactant. The TS-PtCoCu/CNTs with optimal Pt content (∼15 %) exhibited excellent EOR activity, with mass and specific activity of 3.58 A mgPt-1 and 5.79 mA cm-2, respectively, which are 3.8 and 13.5 times higher than those of Pt/C. Compared with the control prepared through the traditional furnace annealing, the catalyst also showed excellent activity and stability. DFT calculations revealed that the TS-PtCoCu/CNTs possesses a downshifted d-band center, weakened CO adsorption and higher OH affinity compared with monometallic Pt, all of which lead to the preferred C1 pathway for EOR. This study demonstrates an ultrafast construction of a highly efficient Pt-Co-Cu ternary catalyst for EOR. Additionally, it provides insights into the reaction mechanism based on structural characterization, electrochemical characterization, and theoretical calculations.

More Reliable Neighborhood Contrastive Learning for Novel Class Discovery in Sensor-Based Human Activity Recognition.

Human Activity Recognition (HAR) systems have made significant progress in recognizing and classifying human activities using sensor data from a variety of sensors. Nevertheless, they have struggled to automatically discover novel activity classes within massive amounts of unlabeled sensor data without external supervision. This restricts their ability to classify new activities of unlabeled sensor data in real-world deployments where fully supervised settings are not applicable. To address this limitation, this paper presents the Novel Class Discovery (NCD) problem, which aims to classify new class activities of unlabeled sensor data by fully utilizing existing activities of labeled data. To address this problem, we propose a new end-to-end framework called More Reliable Neighborhood Contrastive Learning (MRNCL), which is a variant of the Neighborhood Contrastive Learning (NCL) framework commonly used in visual domain. Compared to NCL, our proposed MRNCL framework is more lightweight and introduces an effective similarity measure that can find more reliable k-nearest neighbors of an unlabeled query sample in the embedding space. These neighbors contribute to contrastive learning to facilitate the model. Extensive experiments on three public sensor datasets demonstrate that the proposed model outperforms existing methods in the NCD task in sensor-based HAR, as indicated by the fact that our model performs better in clustering performance of new activity class instances.

NURBS Interpolator with Minimum Feedrate Fluctuation Based on Two-Level Parameter Compensation.

Feedrate plays a crucial role in determining the machining quality, tool life, and machining time. Thus, this research aimed to improve the accuracy of NURBS interpolator systems by minimizing feedrate fluctuations during CNC machining. Previous studies have proposed various methods to minimize these fluctuations. However, these methods often require complex calculations and are not suitable for real-time and high-precision machining applications. Given the sensitivity of the curvature-sensitive region to feedrate variations, this paper proposed a two-level parameter compensation method to eliminate the feedrate fluctuation. First, in order to address federate fluctuations in non-curvature sensitive areas with low computational costs, we employed the first-level parameter compensation (FLPC) using the Taylor series expansion method. This compensation allows us to achieve a chord trajectory for the new interpolation point that matches the original arc trajectory. Second, even in curvature-sensitive areas, feedrate fluctuations can still occur because of truncation errors in the first-level parameter compensation. To address this, we employed the Secant-based method for second-level parameter compensation (SLPC), which does not require derivative calculations and can regulate feedrate fluctuation within the fluctuation tolerance. Finally, we applied the proposed method to the simulation of butterfly-shaped NURBS curves. These simulations demonstrated that our method achieved maximum feedrate fluctuation rates below 0.01% with an average computational time of 360 us, which is sufficient for high-precision and real-time machining. Additionally, our method outperformed four other feedrate fluctuation elimination methods, highlighting its feasibility and effectiveness.

One-pot synthesis of ultrafine trimetallic PtPdCu alloy nanoparticles decorated on carbon nanotubes for bifunctional catalysis of ethanol oxidation and oxygen reduction.

Bifunctional catalysts for ethanol oxidation reaction (EOR) and oxygen reduction reaction (ORR) with high noble-metal utilization are highly beneficial to direct ethanol fuel cells (DEFCs). This study developed a ternary bifunctional catalyst composed of ultrafine PtPdCu alloy nanoparticles and carbon nanotubes (CNTs) support through a facile surfactant-free solvothermal route. The carboxyl terminal groups on CNTs ensure the confined growth of PtPdCu alloys (∼5 nm) and suppress Ostwald ripening of metallic active sites during electrochemical cycling. Consequently, PtPdCu/CNTs exhibits high mass activity (1.95 A mg-1) and specific activity (4.08 mA cm-2) toward EOR, which are 7.8 and 8.9 times higher, respectively, than those of commercial Pt/C. Furthermore, PtPdCu/CNTs displays superior stability toward EOR compared with its bimetallic counterparts (PtPd/CNTs and PtCu/CNTs). In addition, PtPdCu/CNTs exhibits the highest half-wave potential of 0.888 V among all electrocatalysts, indicating high ORR activity. Density functional theory calculations reveal that Pd and Cu mediate the electronic structure of Pt, leading to enhanced catalytic activity of PtPdCu/CNTs. The excellent catalytic property of PtPdCu/CNTs can also be attributed to the bifunctional effects of Pd/Cu and the interaction between metal and the carbon support. The proposed material is a contribution to the family of efficient ternary-alloy electrocatalysts for fuel cells.

A Parallel Multiobjective PSO Weighted Average Clustering Algorithm Based on Apache Spark.

Multiobjective clustering algorithm using particle swarm optimization has been applied successfully in some applications. However, existing algorithms are implemented on a single machine and cannot be directly parallelized on a cluster, which makes it difficult for existing algorithms to handle large-scale data. With the development of distributed parallel computing framework, data parallelism was proposed. However, the increase in parallelism will lead to the problem of unbalanced data distribution affecting the clustering effect. In this paper, we propose a parallel multiobjective PSO weighted average clustering algorithm based on apache Spark (Spark-MOPSO-Avg). First, the entire data set is divided into multiple partitions and cached in memory using the distributed parallel and memory-based computing of Apache Spark. The local fitness value of the particle is calculated in parallel according to the data in the partition. After the calculation is completed, only particle information is transmitted, and there is no need to transmit a large number of data objects between each node, reducing the communication of data in the network and thus effectively reducing the algorithm's running time. Second, a weighted average calculation of the local fitness values is performed to improve the problem of unbalanced data distribution affecting the results. Experimental results show that the Spark-MOPSO-Avg algorithm achieves lower information loss under data parallelism, losing about 1% to 9% accuracy, but can effectively reduce the algorithm time overhead. It shows good execution efficiency and parallel computing capability under the Spark distributed cluster.

Improved LDTW Algorithm Based on the Alternating Matrix and the Evolutionary Chain Tree.

Dynamic time warping under limited warping path length (LDTW) is a state-of-the-art time series similarity evaluation method. However, it suffers from high space-time complexity, which makes some large-scale series evaluations impossible. In this paper, an alternating matrix with a concise structure is proposed to replace the complex three-dimensional matrix in LDTW and reduce the high complexity. Furthermore, an evolutionary chain tree is proposed to represent the warping paths and ensure an effective retrieval of the optimal one. Experiments using the benchmark platform offered by the University of California-Riverside show that our method uses 1.33% of the space, 82.7% of the time used by LDTW on average, which proves the efficiency of the proposed method.

Real-Time NURBS Interpolation under Multiple Constraints.

NURBS interpolation is superior to traditional linear or circular interpolation in terms of code size, surface quality, and machining efficiency. However, with the increasing demands for high-accuracy and efficient machining, NURBS interpolation has faced a growing number of challenges. Many researchers are actively involved in this field with great interest. Due to the special form of NURBS curve, there is a nonlinear relationship between its curve and arc length; feed fluctuations and mechanical shocks which are caused during the interpolation process will seriously affect the surface accuracy and quality of machined parts. To solve these problems, a real-time NURBS interpolation is proposed under multiple constraints (RNIC) in this paper. First, the formulas of the constrained feedrate under geometric errors, kinematic constraints, drive constraints, and contour errors are given. Then, the two stages for the proposed interpolation are established. The former stage is offline preprocessing stage, which aims to quickly find feedrate sensitive areas (FSAs), while the latter online stage is the real-time interpolation, which is responsible for smoothing the velocity. In the preprocessing stage, we utilized FSA scan module and feedrate adjustment module to detect the FSAs and adjust the feedrate at the start/end of each subsegment by a bidirectional scanning algorithm. Each segment contains acceleration and deceleration (some contains uniform speed) stages, which can be well matched with the processing process of acceleration and deceleration. Finally, according to the proposed method and the adaptive speed adjustment method, the simulation of a "butterfly-shaped" NURBS curve using the S-shaped ACC/DEC algorithm is carried out, which verifies the reliability and effectiveness of the proposed algorithm.

Nano-hybrids of needle-like MnO2 on graphene oxide coupled with peroxymonosulfate for enhanced degradation of norfloxacin: A comparative study and probable degradation pathway.

In this work, 1D MnO2 nano-needles were prepared and grown on the graphene oxide (GO) nano-sheets successfully. The morphology and structure of materials were explored. The MnO2 nano-needles with a length of 200-400 nm were distributed uniformly on the GO nano-sheets. As a result of GO substrate, the MnO2/GO nano-hybrids (MnO2/GO) have the much larger surface area and more surface oxygen-containing functional groups than MnO2 nano-needles, which are beneficial for enrichment and degradation of the norfloxacin (NOR). Results showed that more than 80% NOR was degraded within 20 min at the dose of 10 mM PMS and 0.8 g/L catalysts. Moreover, the optimal pH in MnO2/PMS and MnO2/GO/PMS system were both acidic condition. Furthermore, the mechanism of PMS activation by MnO2/GO was investigated through radical identification using quenching experiments and EPR techniques. According to this, the HSO5- of PMS reacted with Mn (IV)/Mn(III) to form a redox loop, and GO played an important role in the degradation process. Finally, the transformation intermediates of NOR were identified and four probable degradation pathways were speculated. This work would provide a potential contribution towards NOR removal in the environmental remediation.