Fiducial Marks for Alignment in Solder Printed Flexible Printed Circuits

This disclosure describes use of fiducial marks for alignment of solder paste in a solder printing process for manufacture of flexible printed circuits (FPC). Per techniques of this disclosure, two diagonal fiducial marks and a central fiducial mark are placed on a FPC and utilized in the solder printing process for optimal alignment of deposited solder paste. The fiducial marks are placed in a central region of the FPC and have a smaller spacing than conventionally utilized fiducial marks. The central fiducial mark is placed at a location that lies perpendicular to a line connecting the diagonal fiducial marks. Spacing between the fiducial marks is designed to compensate for FPC deformation by limiting the shift in the fiducial marks during deformation of the FPC. During the printing process, the spacing between the FPC and printing stencil is automatically adjusted (scaled) to ensure printing alignment

A Novel Pseudo Nearest Neighbor Classification Method Using Local Harmonic Mean Distance

In the realm of machine learning, the KNN classification algorithm is widely recognized for its simplicity and efficiency. However, its sensitivity to the K value poses challenges, especially with small sample sizes or outliers, impacting classification performance. This article introduces a novel KNN-based classifier called LMPHNN (Novel Pseudo Nearest Neighbor Classification Method Using Local Harmonic Mean Distance). LMPHNN leverages harmonic mean distance (HMD) to improve classification performance based on LMPNN rules and HMD. The classifier begins by identifying k nearest neighbors for each class and generates distinct local vectors as prototypes. Pseudo nearest neighbors (PNNs) are then created based on the local mean for each class, determined by comparing the HMD of the sample with the initial k group. Classification is determined by calculating the Euclidean distance between the query sample and PNNs, based on the local mean of these categories. Extensive experiments on various real UCI datasets and combined datasets compare LMPHNN with seven KNN-based classifiers, using precision, recall, accuracy, and F1 as evaluation metrics. LMPHNN achieves an average precision of 97%, surpassing other methods by 14%. The average recall improves by 12%, with an average accuracy enhancement of 5%. Additionally, LMPHNN demonstrates a 13% higher average F1 value compared to other methods. In summary, LMPHNN outperforms other classifiers, showcasing lower sensitivity with small sample sizes

Recent Progress in Two-Dimensional Materials for Electrocatalytic CO2 Reduction

Electrocatalytic CO2 reduction (ECR) is an attractive approach to convert atmospheric CO2 to value-added chemicals and fuels. However, this process is still hindered by sluggish CO2 reaction kinetics and the lack of efficient electrocatalysts. Therefore, new strategies for electrocatalyst design should be developed to solve these problems. Two-dimensional (2D) materials possess great potential in ECR because of their unique electronic and structural properties, excellent electrical conductivity, high atomic utilization and high specific surface area. In this review, we summarize the recent progress on 2D electrocatalysts applied in ECR. We first give a brief description of ECR fundamentals and then discuss in detail the development of different types of 2D electrocatalysts for ECR, including metal, graphene-based materials, transition metal dichalcogenides (TMDs), metal–organic frameworks (MOFs), metal oxide nanosheets and 2D materials incorporated with single atoms as single-atom catalysts (SACs). Metals, such as Ag, Cu, Au, Pt and Pd, graphene-based materials, metal-doped nitric carbide, TMDs and MOFs can mostly only produce CO with a Faradic efficiencies (FE) of 80~90%. Particularly, SACs can exhibit FEs of CO higher than 90%. Metal oxides and graphene-based materials can produce HCOOH, but the FEs are generally lower than that of CO. Only Cu-based materials can produce high carbon products such as C2H4 but they have low product selectivity. It was proposed that the design and synthesis of novel 2D materials for ECR should be based on thorough understanding of the reaction mechanism through combined theoretical prediction with experimental study, especially in situ characterization techniques. The gap between laboratory synthesis and large-scale production of 2D materials also needs to be closed for commercial applications.publishedVersio

DYNAMICS OF LARGE SYSTEMS OF NONLINEARLY EVOLVING UNITS

The dynamics of large systems of many nonlinearly evolving units is a general research area that has great importance for many areas in science and technology, including biology, computation by artificial neural networks, statistical mechanics, flocking in animal groups, the dynamics of coupled neurons in the brain, and many others. While universal principles and techniques are largely lacking in this broad area of research, there is still one particular phenomenon that seems to be broadly applicable. In particular, this is the idea of emergence, by which is meant macroscopic behaviors that “emerge” from a large system of many “smaller or simpler entities such that ... large entities” [i.e., macroscopic behaviors] arise which “exhibit properties the smaller/simpler entities do not exhibit.” [1]. In this thesis we investigate mechanisms and manifestations of emergence in four dynamical systems consisting many nonlinearly evolving units. These four systems are as follows. (a) We first study the motion of a large ensemble of many noninteracting particles in a slowly changing Hamiltonian system that undergoes a separatrix crossing. In such systems, we find that separatrix-crossing induces a counterintuitive effect. Specifically, numerical simulation of two sets of densely sprinkled initial conditions on two energy curves appears to suggest that the two energy curves, one originally enclosing the other, seemingly interchange their positions. This, however, is topologically forbidden. We resolve this paradox by introducing a numerical simulation method we call “robust” and study its consequences. (b) We next study the collective dynamics of oscillatory pacemaker neurons in Suprachiasmatic Nucleus (SCN), which, through synchrony, govern the circadian rhythm of mammals. We start from a high-dimensional description of the many coupled oscillatory neuronal units within the SCN. This description is based on a forced Kuramoto model. We then reduce the system dimensionality by using the Ott Antonsen Ansatz and obtain a low-dimensional macroscopic description. Using this reduced macroscopic system, we explain the east-west asymmetry of jet-lag recovery and discus the consequences of our findings. (c) Thirdly, we study neuron firing in integrate-and-fire neural networks. We build a discrete-state/discrete-time model with both excitatory and inhibitory neurons and find a phase transition between avalanching dynamics and ceaseless firing dynamics. Power-law firing avalanche size/duration distributions are observed at critical parameter values. Furthermore, in this critical regime we find the same power law exponents as those observed from experiments and previous, more restricted, simulation studies. We also employ a mean-field method and show that inhibitory neurons in this system promote robustness of the criticality (i.e., an enhanced range of system parameter where power-law avalanche statistics applies). (d) Lastly, we study the dynamics of “reservoir computing networks” (RCN’s), which is a recurrent neural network (RNN) scheme for machine learning. The ad- vantage of RCN’s over traditional RNN’s is that the training is done only on the output layer, usually via a simple least-square method. We show that RCN’s are very effective for inferring unmeasured state variables of dynamical systems whose system state is only partially measured. Using the examples of the Lorenz system and the Rossler system we demonstrate the potential of an RCN to perform as an universal model-free “observer”

Leveraging Synergies by Combining Polytetrafluorethylene with Polyvinylidene Fluoride for Solvent-Free Graphite Anode Fabrication

Solvent-free graphite anode is fabricated successfully with the synergistic effect of polytetrafluorethylene (PTFE) and polyvinylidene fluoride (PVDF). PTFE acts as a processing aid reagent to form a self-supporting electrode film, while PVDF acts as a functional binder when PTFE decomposes in the first lithiation process. The solvent-free graphite electrode with high loading of 15 mg cm−2 shows good stability with more than 95% capacity retention after 50 charge/discharge cycles under the current of 0.23 mA cm−2. Electrodes with extra high loading of 27 mg cm−2 (8.2 mAh cm−2) are fabricated and show good stability. Initial coulombic efficiency increases to 89% after prelithiation in the full cell with lithium iron phosphate as cathode. The capacity retention of full cells is more than 80% after 110 cycles under the current of 0.7 mA cm−2 in coin cells. The roll-to-roll production makes the procedure compatible with current commercial lithium-ion batteries production lines, exhibiting great potential for upscaling production.publishedVersio

Solvent-free lithium iron phosphate cathode fabrication with fibrillation of polytetrafluoroethylene

Fabricating electrode for lithium-ion batteries (LiBs) with solvent-free (SF) procedure can save energy and improve electrochemical performance simultaneously. Polymer fibrillation is one of the most promising SF procedures due to its feasibility for upscale production. The hardness of lithium iron phosphate (LFP) impedes its SF fabrication with polytetrafluoroethylene (PTFE) fibrillation. In this study, we successfully expanded PTFE fibrillation for SF LFP electrode fabrication with the help of carbon nanotubes (CNTs). CNTs increase the conductivity of electrode, and act as matrix for LFP particles to ensure relative displacement to further fibrillate PTFE to form self-supporting electrode film when the dry mixture was hot rolled. The SF LFP/hard carbon full cells were fabricated and demonstrated comparable electrochemical performance to slurry casting (SC) fabricated LFP electrode. The initial coulombic efficiency (ICE) of full cell increased to more than 95% after prelithiation.publishedVersio