bluuebunny/crossref_metadata_2025 huggingface dataset:
Thermal and Fast Neutron Irradiation Effects on Additively Manufactured and Wrought Inconel 625
Authors: Andurkar Mohanish , O’Donnell Valentina , Keya Tahmina , Prorok Bart , Gahl John , Thompson Scott M.
Published: September 2023

Since their onset, additive manufacturing (AM) technologies are experiencing substantial growth in their development and practical applications in various industries. AM components of different materials are in demand for building next generation advanced nuclear reactors. In this study, Inconel 625 (IN625) samples fabricated using laser powder bed fusion (L-PBF) AM process were subjected to two types of neutron irradiation experiments. Samples were first irradiated for a continuous 310 hours using full spectrum neutrons, ranging in energy from thermal to fast. Secondly, another set of samples were exposed to 7–17 weeks of only fast neutron irradiations in short sessions. The total fluence experienced during full spectrum and fast neutron irradiations were 7.37 × 1019 neutrons/cm2 and 2.74 × 1015−6.61 × 1015 neutrons/cm2 respectively. Along with L-PBF IN625 samples, wrought IN625 samples were used as a reference material in both radiation experiments. The effect of radiation damage in the form of radiation hardening or embrittlement was quantified by measuring microhardness before and after irradiation. In general, irrespective of neutron irradiation type, results indicate the AM IN625 samples showed more resistance towards radiation hardening or embrittlement defect compared to its counterparts wrought IN625 samples. The results reported in this study will increase the confidence in adopting AM technologies to build critical advance nuclear components in the future.
3D Printed Bioinspired Hierarchical Surface Structure With Tunable Wettability
Authors: Rahman M. M. Towfiqur , Joyee Erina Baynojir
Published: September 2023

Nature has examples of impressive surfaces and interfaces with diverse wettability stemming from superhydrophilicity to superhydrophobicity. The multiscale surface structures found in biological systems generally have high geometric complexity, which makes it challenging to replicate their characteristics, especially using traditional fabrication techniques. It is even more challenging to fabricate such complex microstructures with tunable wettability. In this paper, we propose a method to tune the wettability of a micro-scale surface by changing the geometrical parameters of embedded micro-structures in the surface. By taking inspiration from an insect (springtails), we designed micro pillar arrays with different roughness by adjusting geometric parameters such as reentrant angle, pitch distance, and the number of spikes and pillars. This study shows that, by changing geometrical parameters in micro-scale, the apparent contact angle, and hence the surface wettability can be calibrated. The micro-scale pillars were fabricated using a precise micro direct light processing (μDLP) three-dimensional (3D) printer. Different printing parameters were studied to optimize the geometric parameters to fabricate 3D hierarchical structures with high accuracy and resolution. The relationship between the wettability of the surface and the geometrical parameters was predicted using a modified Cassie’s model and experimentally validated. The largest apparent contact angle in our experiments is up to 160°, with pillars of 0.17mm height and 0.5 mm diameter, 55° reentrant angle, and a spacing of 0.36 mm between pillars. The lowest contact angle is ∼35° by reducing the pillar size and spacing. By controlling the size of different features of the pillar, pillar number, and layout of the mushroom shaped micropillars, the wettability of the surface is possible to be tuned from a highly nonwetting liquid/material combination to highly wetting material. Such wettability tuning capability expands the design space for many biomedical and thermofluidic applications.
Rapid 3D Printing of Nanoporous Copper Powders via Micro-Clip
Authors: Liu Luyang , Kublik Natalya , Azeredo Bruno , Chen Xiangfan
Published: September 2023

Three-dimensional (3D) printing of metal components through powder bed fusion, material extrusion, and vat photopolymerization, has attracted interest continuously. Particularly, extrusion-based and photopolymerization-based processes employ metal particle-reinforced polymer matrix composites (PMCs) as raw materials. However, the resolution for extrusion-based printing is limited by the speed-accuracy tradeoff. In contrast, photopolymerization-based processes can significantly improve the printing resolution, but the filler loading of the PMC is typically low due to the critical requirement on raw materials’ rheological properties. Herein, we develop a new metal 3D printing strategy by utilizing micro-continuous liquid interface printing (μCLIP) to print PMC resins comprising nanoporous copper (NP-Cu) powders. By balancing the need for higher filler loading and the requirements on rheological properties to enable printability for the μCLIP, the compositions of PMC resin were optimized. In detail, the concentration of the NP-Cu powders in the resins can reach up to 40 wt% without sacrificing the printability and printing speed (10 μm·s−1). After sintering, 3D copper structures with microscale features (470 ± 140 μm in diameter) manifesting an average resistivity of 150 kΩ·mm can be realized. In summary, this new strategy potentially benefits the rapid prototyping of metal components with higher resolution at faster speeds.
Job Placement for Cooperative 3D Printing
Authors: Weber Daniel H. , Zhou Wenchao , Sha Zhenghui
Published: September 2023

Cooperative 3D Printing (C3DP), an additive manufacturing platform consisting of a swarm of mobile printing robots, is an emerging technology designed to address the size and printing speed limitations of conventional, gantry-based 3D printers. A typical C3DP process often involves several interconnected stages, including project/job partitioning, job placement on the floor, task scheduling, path planning, and motion planning. In our previous work on project partitioning, we presented a Z-Chunker, which vertically divides a tall print project into multiple jobs to overcome the physical constraints of printers in the Z direction, and an XY Chunker, to partition jobs into discrete chunks, which are allocated to individual printing robots for parallel printing. These geometry partitioning algorithms determine what is to be printed, but other information, such as when, where, and in what order chunks should be printed, is required to carry out the print physically. This paper introduces the first Job Placement Optimizer for C3DP based on Dynamic Dependency List schedule assignment and Conflict-Based Search path planning. Our algorithm determines the optimal locations for all jobs and chunks (i.e., subtasks of a job) on the factory floor to minimize the makespan for C3DP. To validate the proposed approach, we conduct three case studies: a simple geometry with homogeneous jobs in the Z direction and two complex geometries (one with moderate complexity and one relatively more complex) with non-homogeneous jobs in the Z direction. We also performed simulations to understand the impact of other factors, such as the number of robots, the number of jobs, chunking orientation, and the heterogeneity of prints (e.g., when chunks are different in size and materials), on the effectiveness of this placement optimizer.
Evolution of Grain Refinement and Phase Transformation Based on Stress-Strain Induced Incremental Model in Machining of Titanium Alloy
Authors: Yang Peiqiang , Zhang Xueping , Yao Zhenqiang , Shivpuri Rajiv
Published: September 2023

Grain refinement occurs on the machined surface and adiabatic shear band (ASB) through severe plastic deformation (SPD) in the high-speed machining (HSM) Ti-6Al-4V alloy, resulting in the formation of desirable ultrafine grain structure. The extremely high temperature changing rate induces the transformation of the original α-phase into β-phase, which then forms martensitic phase after cooling process. The altered microstructure has different effects on the material properties and machined surface integrity. Therefore, the established model has received the increasing attentions to accurately predict microstructural evolution. This research proposes a strain-induced incremental model of grain dynamic recrystallization (DRX) evolution based on the conventional Johnson-Mehl-Avrami-Kolmogorov (JMAK) model. The model emphasizes the contribution of large strain to grain refinement during the machining of Ti-6Al-4V alloy. In addition, a stress-induced phase transformation increment model is proposed to describe the phase evolution in machining since high stress can effectively reduce the initial temperature in transiting α-phase to β-phase and the martensite phase transition temperature. Subsequently, the orthogonal cutting simulations of Ti-6Al-4V alloy are carried out to validate the proposed model by comparing with the existing research results. The simulation results indicate that the average grain size in the ASB is increased from 46.2% to 54.2% and the average grain size at the machined surface decreased from 85% to 83% with the cutting speed range of 5–20 m/s. The average grain size at the machined surface is decreased from 83% to 78% with the tool flank face wear (VB) ranging from 0.00 mm (fresh) to 0.08 mm. The decreased temperature has an inhibitory effect on the grain refinement process of the ASB and the machined surface within the range of initial temperatures from −150 °C to 25 °C.
Submicron Motion Measurement Based on Nonlinear Identification of Diffractive Optical Features
Authors: Su Taiyu , Zhang Zhijing , Xiong Jian , Li Erbo , Saren Qimuge
Published: September 2023

Precision positioning is a critical technology in precision machinery manufacturing, and precision displacement measurement is the basis of precision positioning. Optical displacement measurement technology currently occupies the dominant position of precision displacement measurement. In contrast, the existing precision optical measurement methods are easily restricted by environmental interference, high cost, complex structure, and difficult integration. A submicron-level accuracy motion measurement method proposed in this paper is based on the nonlinear identification of diffractive optical features with the help of spatial information contained in the phenomenon of Fresnel diffraction. According to this method, the relationship between the aperture position and the diffraction image in Fresnel circular aperture diffraction is used to establish a nonlinear mapping model of the relationship between diffractive optical features and the aperture position by a convolutional neural network to convert the plane displacement measurement into the problem of diffractive optical features recognition, to realize the high-precision motion displacement measurement of two degrees of freedom on the same plane. An experimental setup is developed and experimentally tested to verify the scheme’s feasibility. The experimental results show that the mapping relationship between diffractive optical features and aperture position can be adequately fitted by CNN to achieve motion measurement with submicron-level accuracy. This method of precision measurement based on diffractive optical features provides a new research idea and solution for motion measurement with submicron-level accuracy.
Design and Construction of a Belt-Assisted Vertical Extrusion Based Fused Deposition Modeling 3D Printer for Automated Part Removal
Authors: Brooks Jacob , Rahman M. M. Towfiqur , Joyee Erina Baynojir
Published: September 2023

In recent years, Fused Deposition Modeling (FDM) has become one of the most used three-dimensional (3D) printing processes because of its cost effectiveness and universal approach towards user-friendliness. FDM uses a layering process to build 3D models layer-upon-layer using a varied range of materials from thermoplastic to pharmaceutical materials. However, FDM has a considerable number of challenges to overcome, especially for its use in line production. One possible solution for a more line-production friendly FDM process could be the use of a continuous moving build surface, similar to more conventional line production processes. In this paper, we propose a customized FDM 3D printing system with a moving belt-style build surface and kinematics that allow layers to be printed parallel to the build surface. Unlike other existing belt-FDM systems, the developed FDM printer will deposit filament perpendicular to the bed, allowing the user to utilize conventional 3D printing slicing software, depend less on support structures, and design parts in a traditional manner. Moreover, after completion, these parts can be ejected automatically to allow repeated prints without human intervention, making the printing process faster and more cost-effective. A belt-assisted vertical extrusion based FDM prototype 3D printer has been designed, constructed, and then tested to gain experimental data on continuous 3D printing using thermoplastic polymers as a printing material. Results of testing and experimentation verified certain key design elements and how they could improve continuous printing and automated 3D printed part removal. Experiments also revealed how nozzle temperature, bed temperature, and the moving speed of the nozzle introduce challenges not seen in traditional FDM printers with static beds.
Anomaly Detection for Industrial Robot Prognostics and Health Management
Authors: Fattah Gerald , Newton David , Qiao Guixiu , Leber Dennis D.
Published: September 2023

Industrial robots have continued to play important roles in automation for smart manufacturing. Effective maintenance strategies are essential for successful robot operation. However, maintenance can be costly which has led manufacturers to explore different ways to monitor and evaluate the health of their robot workcell during operation. Successful methods to assess robot health rely on capturing and analyzing significant amounts of continuously generated data from the workcell. Various data streams relevant to a robot workcell may be available such as robot controller data, programmable logic controller data, maintenance logs, and part quality data. Although these data streams can reveal information about the workcell, the large volume and complexity of the data can prove difficult to analyze. Researchers at the National Institute of Standards and Technology (NIST) have developed a test method and accompanying unsupervised learning anomaly detection procedure to assess the health of robot workcells. The approach involves defining a distance metric which is then passed to a robust outlier detection step. Performance of the approach demonstrated on several types of simulated robot failures and compared against different popular anomaly detection algorithms.
Magnetically Assisted Laser Induced Plasma Micromachining (M-LIPMM)
Authors: Cleeman Jeremy , Guo Weihong , Malhotra Rajiv
Published: September 2023

Laser Induced Plasma MicroMachining (LIPMM) uses a pulsed laser to create a plasma spot inside a dielectric liquid and then uses this plasma for machining. Compared to direct laser machining LIPMM enables a wider multimaterial capability, greater machined depth for the same width, lesser damage, and greater throughput. This paper explores Magnetically assisted Laser Induced Plasma MicroMachining (M-LIPMM) in which an external magnetic field is applied to the plasma during machining. We focus on the effects of using a single magnet. The dimensions of the machined features are characterized as a function of the magnetic pole orientation, pulse energy, laser speed, and pulse frequency. We find that M-LIPMM adds an effective magnetic lens to a laser that allows concurrent width reduction and depth increase, greater throughput, and enhanced geometric flexibility, beyond the capabilities of LIPMM.
A Monte-Carlo Method for Evaluating the Economic Performance of Plastics Recycling Systems Using Historical Pricing
Authors: Nandimandalam Hariteja , Costello Christine , Mendis Gamini P.
Published: September 2023

The management of plastic waste is a considerable emerging global concern. Conventional plastics recycling rates range from 5–10% in the US, which results in considerable material being sent to landfill. New sorting technologies using artificial intelligence have the potential to dramatically increase the ability of waste management companies to sort out valuable fractions of plastic waste and create high-purity streams for secondary markets. However, several fractions of the plastics recycling stream, i.e., low-density polyethylene, polypropylene, and polystyrene, do not have well-developed markets. These materials may be sent to pyrolysis facilities for thermal recycling, but the economics of the pyrolysis industry are currently uncertain. This work aims to identify the breakeven price of the plastic fractions that would be sent to pyrolysis in order to determine the economic viability of the sorting facility. The work will use Monte Carlo analysis to evaluate several scenarios and understand how compositional variation, price variability, and facility attributes affect the breakeven price. The presorting conditions in Materials Recovery Facilities strongly affects the profitability of the sorting facility. High residual high density polyethylene (HDPE) and polyethylene terephthalate (PET) content in presorted bales are important revenue drivers for the facility, even in presorted 3-7 bales. Key variables that lead to profitability include a high sale price of polyethylene terephthalate, a high sale price of high-density polyethylene, and if polypropylene can be sold to recycling markets (as opposed to pyrolysis facilities).
Suppression of Catalyst Diffusion Into Alumina Support in Dynamic Chemical Vapor Deposition of Carbon Nanotube
Authors: Tomaraei Golnaz , Lee Jaegeun , Kim Seung Min , Abdulhafez Moataz , Bedewy Mostafa
Published: September 2023

The utilization of rapid thermal processing capabilities in chemical vapor deposition of carbon nanotubes (CNTs) enables creating dynamic growth recipes engineered to independently control each of the following stages: catalyst nanoparticle preparation by thin film dewetting, and nucleation of CNTs by catalytic growth. While higher temperature pretreatment during the catalyst preparation step enables extending the catalytic lifetime to greatly increase the growth yield by more than three folds, revealing the mechanism of such improvement is crucial. Here, we combine multilayer modeling of ellipsometry results with cross-section scanning transmission electron microscopy (STEM), X-ray diffraction, and scanning electron microscopy (SEM), in order to quantify the evolution of the iron nanocatalyst on alumina thin film stacks as a function of both temperature and time of heating in a reducing environment. Cross section STEM shows the diffusion of iron into the underlying alumina support layer, and our ellipsomettry results confirm that this diffusion is suppressed at high temperature of 900 °C, compared to 700 °C. Importantly, our measurements support the hypothesis that both the loss of iron catalyst from the surface and the rate of subsurface diffusion are higher at the low annealing temperatures, because of a lower density alumina phase. We utilize SEM to estimate the percentage of iron on the surface after dewetting, which is used in the top-most layer in the stack for ellipsometry modeling. Moreover, we show that an interface layer of both iron and alumina is necessary to include in order to accurately model the multilayer system. By comparing two different sets of samples with 1 nm iron on 10 nm alumina as well as with 10 nm iron on 100 nm alumina, we confirm that these findings can be extended to different ranges of thickness.
Investigating Electrode Design Methodology for Improving Machining Performance in Silicon Using Die Sinking EDM
Authors: Karim Mahmud Anjir , Nafi Muhammad Abdun , Jahan Muhammad P.
Published: September 2023

Silicon is the material that is most frequently utilized in the electronic industries because of its distinctive properties which encompasses from age resistance to high temperature stability. However, it is extremely challenging to machine silicon using conventional machining processes that employs traditional cutting force. As a result, non-conventional machining techniques have gained popularity and Electrical Discharge Machining (EDM) is one of the non-conventional machining techniques that has enormous potential in terms of machining silicon. However, the slow rate of material removal is the major drawback of machining silicon using EDM. This study seeks to investigate unique electrode designs and explore their efficacy in terms of improving material removal rate (MRR), reducing electrode or tool wear rate (TWR), and improving quality of the features machined by those electrodes. In this study, graphite electrodes with different arrays (1 × 4, 2 × 4 and 3 × 4) and leg heights (0.2” and 0.3” of electrode legs) were made using milling machine and were used in the die sinking EDM process to investigate the effects of heights and arrays on the material removal rate, tool wear rate and dimensional accuracy of the fabricated features keeping the EDM parameters constant. It has been observed that the increase of arrays of electrode legs results in shorter machining times per leg due to improve flushing conditions for both cases of electrode height. For lower arrays of electrode legs, smaller height (0.2″) performs better in terms of material removal rate. At 3 × 4 arrays of electrode, both cases of height of electrode show almost identical material removal rate. In case of dimensional accuracy, the quality of features improved with increased arrays of electrode legs for both leg heights (0.2” and 0.3″) of electrode. However, for overall accuracy of the features, height does not have significant effect as both performs quite similar. In this study, electrode wear rate is found negative due to carbon deposition on the graphite electrode. With the increased arrays of electrode legs, there is a decrease in carbon deposition per legs of electrode for both cases of electrode height. In terms of the effects of electrode leg height, each case of arrays performs quite alike.
Fabrication of Superhydrophobic Surfaces Using Glancing Angle Deposition
Authors: Qu Chuang , McNamara Shamus , Walsh Kevin
Published: September 2023

This paper introduces the fabrication of superhydrophobic surfaces using glancing angle deposition (GLAD). GLAD is an advanced physical vapor deposition technique, during which the tilt and rotation of the substrate are maneuvered with some algorithm. GLAD has the unique advantage of creating three-dimensional nanofeature arrays, comparing to conventional top-down nanofabrication techniques. Various nanofeatures created by GLAD have been reported, including pillars, springs, chevrons, ribbons, and those structures as templates for creating nanoporous membranes. Superhydrophobicity is nature inspired, such as from lotus leaf and cicada wings, which are with highly porous micro/nanostructures that reduce the surface energy of the surfaces to achieve the water repellent property. The ability to create hierarchical and porous nanostructures makes GLAD the perfect candidate for creating superhydrophobic surfaces. GLAD processes for superhydrophobic surfaces is proposed by other research groups. However, the processes are flawed: either time consuming, or need to be assisted by templates that are challenging to be made. This paper will introduce a new approach with GLAD for creating superhydrophobic surfaces. This approach does not need the seeding layer that are proposed by for creating the hierarchical nanoporous surfaces. Superhydrophobic surfaces are achieved with the water contact angle of 155 degrees. More GLAD surfaces with higher/lower water contact angles by different GLAD recipes are created and analyzed. The superhydrophobic surfaces are potentially used for applications such as anti-icing surfaces, self-cleaning surfaces, and antimicrobial surfaces.
Highly Parsimonious Multi-Fidelity Learning of Process Parameter-Performance Relationships: A Case Study With Fused Filament Fabrication
Authors: Cleeman Jeremy , Malhotra Rajiv
Published: September 2023

Modeling the effects of the process parameters on the process or product performance is critical for predictive process design and control. While physics-based modeling has long been the path to such predictions, machine learning models are of increasing interest recently. But the data-hungry nature of machine learning entails prohibitive experimental cost, is hindered by large oversimplifications of computationally efficiency analytical models, or is limited by the time and cost of creating and executing more accurate numerical models. The situation becomes worse for newer processes or material for which the underlying coupled physics is often not fully known, thus decelerating process or material deployment. Transfer learning has been proposed as a solution for this problem, but the above issues remain since a source model of sufficient fidelity is still needed. This paper takes a first stab at this issue. We propose a physics-informed transfer learning that combines a large amount of data from an extremely low-fidelity but equally computationally efficient process model based on first principles with a small experimental dataset that represents ground truth. We demonstrate this approach by creating a support vector regression model of printed line width in Fused Filament Fabrication. Orders of magnitude reduction in computational cost and 60% reduction in experimental data needed is achieved, while retaining similar or lower error as compared to naïve learning on a much larger experimental dataset. We demonstrate the importance of the number and location of high-fidelity experimental data points and discuss future work in this context.
A Machine Learning Genetic Algorithm (ML-GA) Approach to Optimize Process Parameters of Trochoidal Toolpath During Hybrid-Electrochemical Magnetorheological (H-ECMR) Finishing
Authors: Rajput Atul Singh , Das Manas , Kapil Sajan
Published: September 2023

Hybrid-Electrochemical Magnetorheological (H-ECMR) Finishing process is an advanced surface finishing technique that produces surface roughness (Ra) up to a few nanometers required for the implants. In this study, duplex stainless steel (DSS), a biomaterial constituting ferrite and austenite in equal proportion, is considered as the workpiece. DSS is widely used for its high corrosion resistance compared with other grades of stainless steel biomaterials. Furthermore, optimization of the process parameters for the trochoidal, a high-speed finishing toolpath, is carried out to achieve optimum surface roughness parameters during H-ECMR finishing. The surface roughness parameters (i.e., average Surface roughness (Ra). Kurtosis (Rku), and Skewness (Rsk)) are attained from the integration of the Design of Experiment (DOE), Response Surface Methodology (RSM), and Machine Learning Genetic Algorithm (ML-GA). DOE is utilized to plan the experiments based on Central Composite Design (CCD) using input process parameters of the H-ECMR finishing process. The regression equation is developed with the RSM, and its optimization study is performed with ML-GA. This paper aims to analyze the impact of the process parameters for the H-ECMR finishing process on different surface roughness parameters.
Utilizing Vision-Based Object Detection Algorithms in Recognizing Uncommon Operating Conditions for CNC Milling Machine
Authors: Choudhari Sahil J. , B. J. Sujay , Singh Swarit Anand , Desai K. A.
Published: September 2023

Although Computer Numerical Control (CNC) machines were designed to perform tasks with the least human intervention, operator involvement is mandatory to ensure fault-free operations. Numerous technological solutions utilizing Artificial Intelligence, sensor fusion, Internet of Things (IoT), machine vision, etc., have been developed for process, component, and machine monitoring to impart smartness and autonomous operating abilities. The primary focus of these solutions is to monitor process faults such as tool wear, chatter, static deflections, and cutting forces to assist the operator in minimizing the consequences. The present work develops a vision-based solution for identifying uncommon process abnormalities like improper coolant flow, chip clogging, and tool breakage during CNC milling. The proposed solution replicates the task of a machine operator in identifying these faults and assists in fault-free operations. The study explores the feasibility of utilizing classical and deep learning-based object detection algorithms while developing these solutions. The classical image processing algorithm is ineffective during dynamic process conditions. The deep learning-based algorithm, with an average precision of about 0.75, showed proficiency in abnormalities detection. A Graphical User Interface (GUI) has been developed and integrated with the CNC milling machine to provide an interactive in-process monitoring tool. It is demonstrated that the proposed solution can reduce dependence on a machine operator while monitoring these faults.
Investigation of the Interfacial Adhesion Strength of Parts Additively Manufactured on Fabrics
Authors: Blais Maxwell , Tomlinson Scott , Khoda Bashir
Published: September 2023

This research first presents a method of peel testing developed by the researchers to characterize the strength of the interface between fabric and additively manufactured material. Experimentation is next presented that characterizes the interfacial strength relative to a set of parameters which include fabric fiber morphology, thickness of sizing applied to fabric, 3D printer bed temperature, and angle of additive manufacturing relative to the fabric warp direction. The interface strength within the parameter space presented was then searched and found to have a maximum of 5.18N per mm using a novel set of parameters. This interface strength indicates the method of additive manufacturing direction on fabric may be suitable for use in a broader range of applications than previously proven feasible.

Relatively rough, thick, and loose weave fabrics were found to promote interface strength compared to smoother, thinner, and finer woven fabrics. Relatively higher bed temperatures also promoted higher interface strength. Sizings on the fabric were found to promote interface strength with relatively smooth, thin, or fine fabrics which don’t themselves promote high mechanical interlocking. Using these research findings, interface strength between fabric and additively manufactured material can be modified to suit the application.
A Comparative Analysis of Cathode Stripping Methods for Direct Recycling of Spent Li-Ion Batteries
Authors: Xiao Yaohong , Su Jinrong , Chen Lei
Published: September 2023

The cathode is the most valuable battery component rich in precious metals and, thus, is the leading recycling target in spent lithium-ion batteries. In particular, the emerging development of direct recycling techniques requires the non-destructive spent cathode as feedstock; however, posing a challenge to effectively strip the spent cathode powders without destroying the cathode structure after disabling the binders. In this study, we compare four cathode stripping methods, including solvent dissolution & magnetic stirring, solvent dissolution & sonication, heat treatment & curling-uncurling, and solvent dissolution & resonant acoustic vibration. Our results show that solvent dissolution & resonant acoustic vibration-based cathode stripping achieves an efficiency of up to 92%, without introducing impurities such as small aluminum fragments and powders. These findings demonstrate the potential of resonant acoustic vibration-based cathode stripping for scaling up the cathode powder recovery and direct recycling of spent Li-ion batteries.
Application of Digital Twins to Laser Powder Bed Fusion Additive Manufacturing Process Control
Authors: Yeung Ho , Kim Felix , Donmez Alkan
Published: September 2023

Digital twins for additive manufacturing (AM) have drawn much research attention recently, thanks to advancements in artificial intelligence and machine learning. Machine learning takes the process and measurement data from the manufacturing process to build data-driven models instead of physics-based descriptive models. The latter are usually hard to obtain for complex AM processes such as laser powder bed fusion. This study proposed a digital twin framework for the laser powder bed fusion AM process control and optimization. The framework is created based on the recently developed advanced point-wise scan control method. It consists of four components: digital twin of process design, digital twin of process control, digital twin of process monitoring, and digital twin of printed part. Their construction is detailed, and potential applications are demonstrated/discussed.
A Bionic Design With Wing Structures to Assist Flexible Microelectrode Implantation
Authors: Zhang Leihan , Wang Yi , Cai Yi
Published: September 2023

Microelectrodes and microprobes are promising tools to explore brains at the cellular level or restore function in the nervous system. While making electrodes ultra-small and flexible is a trend with many merits in biomedical implants, it also brings a considerable challenge to insert them successfully and efficiently due to the buckling and deflection of the electrodes. Similar problems also exist in nature, like mosquito proboscis behavior and Hymenoptera oviposition. They develop a series of mechanisms to overcome the problems, such as reducing effective length, increasing stress concentration, guide-assisted insertion, reciprocating penetration, etc. Based on these mechanisms, a bionic design is presented where two wing structures are attached to the planar-type neural probe. The principles and benefits of the design are elaborated, and prototype dummy probes are fabricated. Compression buckling tests and insertion tests are conducted on tissue phantom, showing that the bionic design can enhance the stiffness of the probe and reduce the deflection angle effectively, improving the accuracy and efficiency of probe implantation. The presented design opens new approaches for the development of novel flexible microelectrodes and microprobes.
Study on Dicing Mechanism for PZT-4 Piezoelectric Ceramics Composite
Authors: Zhou Yang , Liu Yao , Jinjie Zhou , Guo Zhanling
Published: September 2023

The dicing and filling is the most commonly method to fabricate the piezoelectric composite by the removing parts of the piezoelectric ceramics and filling with the polymer, which can improve the piezoelectricity of piezoelectric ceramics tremendously. The ceramics dicing quality affect the composite piezoelectric performance and the dicing mechanism is not clear now. In this study, the experiments were conducted to reveal the influence of the dicing parameters such as wheel speed and feed rate on the surface quality (including surface roughness, kerf width, surface damage width and height). The results show that with the increase of wheel speed and the decrease of feed rate, the surface roughness and surface damage width and height decrease gradually, while the kerf width is opposite. The feed rate has the greatest influence on the surface quality, followed by the wheel speed. In addition, the surface roughness of each ceramic dicing surface decreases from top to bottom. This study reveals the dicing mechanism of the PZT-4 and laid a fundamental understanding for piezoelectric composite performance.
Effects of Electrolyte Bath Composition on the Surface Properties and Nickel Content of Zinc-Nickel Electroplating
Authors: Vispute Suprita , Sundaram Murali , Yang Jung Ho , Iyyer Nagaraja , Phan Nam D. , Kittur Madan G.
Published: September 2023

Electroplating is used as a surface protection process in several industries. The well-known cadmium electroplating needs an alternative due to the environmental hazards, and occupational safety and health risks arising from the carcinogenic nature of cadmium. Zinc-nickel alloy electroplating has the potential to replace cadmium plating in surface protection applications. This paper investigates the effect of acidic and alkaline electrolyte baths on the composition and surface properties of the zinc-nickel electroplating on 300M steel. Five electrolyte baths with different levels of chloride and citrate content were studied. Scanning Electron Microscope (SEM) imaging of specimens was done to study the surface morphology. The composition of nickel in the plating was determined using Energy Dispersive Spectroscopy (EDS) while the phase analysis was conducted by X-ray Diffraction (XRD). Incorporation of chloride in the electrolyte baths increased the nickel composition in the deposit while citrate addition reduced it in the deposit obtained from the acidic electrolyte. Surface roughness was measured using a stylus profilometer. It was observed that smoother surface was obtained with alkaline electrolyte baths. The surface wettability was obtained by measuring the contact angle between deionized water and the plating. Alkaline electrolytes produced higher surface wettability in the plating than acidic electrolytes. Also, the wettability of the surface was found to increase when the alkaline electrolyte was used without any chloride accelerators.
Asymptotic behavior of resonant frequencies in resonant ultrasound spectroscopy
Authors: Farzbod Farhad , Holycross Casey M.
Published: September 2023

Resonance ultrasound spectroscopy is a non-destructive technique used to assess materials' elastic and anelastic properties. It involves measuring the frequencies of free vibrations in a carefully prepared sample to extract material properties. In this paper, we investigate the asymptotic behavior of eigenfrequencies. Our primary focus is on analyzing the asymptotic behavior of eigenfrequencies, aiming to understand their rate of growth and convergence. We also make observations regarding the impact of elastic constants on eigenfrequencies.
A Bayesian inversion for emissions and export productivity across the end-Cretaceous boundary
Authors: Cox Alexander A. , Keller C. Brenhin
Published: September 2023

The end-Cretaceous mass extinction was marked by both the Chicxulub impact and the ongoing emplacement of the Deccan Traps flood basalt province. To understand the mechanism of extinction, we must disentangle the timing, duration, and intensity of volcanic and meteoritic environmental forcings. In this study, we used a parallel Markov chain Monte Carlo approach to invert for carbon dioxide (CO
2
) and sulfur dioxide (SO
2
) emissions, export productivity, and remineralization from 67 to 65 million years ago using the LOSCAR (Long-term Ocean-atmosphere-Sediment CArbon cycle Reservoir) model. Our results closely match observed and proxy data and suggest decoupled CO
2
and SO
2
emissions, a two-step decline in export productivity with a protracted recovery, and no clear volatile impulse at the boundary. More broadly, our methods provide a potential path forward for efficient parallel inversion of complex Earth system models.
An epigenetic clock in plants
Authors: Satyaki P. R. V.
Published: September 2023

DNA methylation can identify evolutionary relationships among close plant lineages
Relative resistance of patient-derived envelope sequences to SERINC5-mediated restriction of HIV-1 infectivity
Authors: Nkuwi Emmanuel , Judicate George P. , Tan Toong Seng , Barabona Godfrey , Toyoda Mako , Sunguya Bruno , Kamori Doreen , Ueno Takamasa
Published: September 2023

A host cellular transmembrane protein, SERINC5, inhibits HIV-1 infectivity when incorporated into progeny virions. Recent studies suggest that certain Envelope glycoproteins can resist SERINC5-mediated restriction. However, the underlying mechanism of Envelope glycoprotein-mediated resistance to SERINC5 restriction remains unclear. Here, we investigated the extent of sensitivity of patient-derived HIV-1 Envelope sequences to SERINC5-mediated restriction and examined Envelopes’ characteristics in relation to SERINC5. A
nef
-deficient HIV-1 reporter was pseudotyped with Envelope sequences isolated from a total of 50 Tanzanians infected with non-B HIV-1 subtypes in the presence and absence of SERINC5 expression. The infectivity of resultant pseudoviruses was differentially reduced by SERINC5 by a median of 5.1-fold (IQR: 3.2–8.6) including five outliers showing ≥20-fold reduction, whereas the pseudovirus with the control NL4-3 envelope was reduced by 64-fold. The pseudovirus sensitivity to SERINC5-mediated restriction differed significantly among the subtypes of the envelope sequences but was not associated with any other Envelope characteristics or clinical parameters tested. Within some hosts, the pseudovirus sensitivity to SERINC5 varied substantially among Envelope sequences, with sensitive ones (as defined by ≥20-fold reduction) being underrepresented. Analysis of chimeric constructs between intra-host clones revealed that both N- and C-terminus of the Envelope sequences were responsible for SERINC5-mediated restriction. Taken together, these results demonstrate that the majority of naturally occurring Envelope sequences across multiple subtypes are relatively less sensitive to SERINC5-mediated restriction of infectivity and that naturally occurring polymorphisms at N- and C-terminal parts are important for this sensitivity.

IMPORTANCE

Pathogenesis of HIV-1 is enhanced through several viral-encoded proteins that counteract a range of host restriction molecules. HIV-1 Nef counteracts the cell membrane protein SERINC5 by downregulating it from the cell surface, thereby enhancing virion infectivity. Some subtype B reference Envelope sequences have shown the ability to bypass SERINC5 infectivity restriction independent of Nef. However, it is not clear if and to what extent circulating HIV-1 strains can exhibit resistance to SERINC5 restriction. Using a panel of Envelope sequences isolated from 50 Tanzanians infected with non-B HIV-1 subtypes, we show that the lentiviral reporters pseudotyped with patient-derived Envelopes have reduced sensitivity to SERINC5 and that this sensitivity differed among viral subtypes. Moreover, we found that SERINC5 sensitivity within patient-derived Envelopes can be modulated by separate regions, highlighting the complexity of viral/host interactions.
Supplemental Material: New biogeochemical insights into Mesozoic terrestrial paleoecology and evidence for omnivory in troodontid dinosaurs
Authors: Cullen Thomas , Cousens Brian L. , et al.
Published: September 2023

Table S1: Geochemical results organized by sample. Table S2: Data used for REE comparison plot.
Management dilemma in Thoracoamniotic Shunt Migrations
Authors: Tan Audrey Poh Poh , Tan Bobby , Wright Ann , Kong Juin Yee
Published: September 2023

Shunt migration is a rare but significant complication of thoracoamniotic shunting, an intervention widely used for fetal pleural effusion. We describe a case of a term infant noted antenatally to have fetal hydrothorax that was managed with thoracoamniotic shunting but complicated by shunt migration. We also present the current literature on risk factors, complications and management of intrathoracic shunt migration. The majority of shunt migration cases are managed conservatively with no untoward postnatal sequelae, but surgical removal of the migrated shunt has been used for associated clinical complications, if visceral damage is suspected or if postnatal thoracic surgery is indicated for other reasons. We advocate an approach of conservative management for asymptomatic infants, where possible, to avoid unnecessary surgical and anaesthetic risks to very young, often already compromised children. However, further studies are still required to determine optimal management after shunt migration has occurred to ensure the best outcome.
Effect of intranasal sufentanil on acute post-traumatic pain in the emergency department: a randomised controlled trial
Authors: Malinverni Stefano , Kreps Bernard , Lucaccioni Thibault , Bouazza Fatima-Zohra , Bartiaux Magali , Plumacker Alain , Pascu Andreea , Youatou Towo Pierre
Published: September 2023

Background

Intranasal sufentanil is a potent opioid which can be used in patients with traumatic injuries presenting to the ED. Although previous studies have demonstrated the superiority of intranasal sufentanil over intravenous morphine in terms of pain relief, its clinical superiority in patients with traumatic injuries receiving adequate multimodal analgesia with acetaminophen and non-steroidal anti-inflammatory drugs is uncertain. We compared pain relief offered by intranasal sufentanil with that offered by oral and intravenous opioids in patients with acute traumatic injuries also receiving a specified regimen of non-opioid treatment.

Methods

In this single-centre, open-label, parallel-group, randomised controlled superiority trial conducted between January 2020 and February 2022, trauma patients presenting to the ED with a pain score of ≥7 on a visual analogue scale (VAS) were randomised to receive either intranasal sufentanil or other oral/intravenous opioids alongside oral/intravenous acetaminophen and non-steroidal anti-inflammatory drugs. The primary outcome was reduction in VAS score 15–20 min after randomisation.

Results

An intention-to-treat analysis included 170 out of 205 patients screened for inclusion. The intranasal sufentanil group (83 patients) showed a significantly greater reduction in pain when compared with the oral/intravenous opioid group (87 patients) 15–20 min after randomisation (reduction in VAS score 3.0 (IQR 1.7–5.0) vs 1.5 (IQR 0.9–3.0); p<0.001). Similarly, a greater reduction in pain was observed in the intranasal sufentanil group 60 min after randomisation (5.0 (IQR 3.0–7.0) vs 3.0 (IQR 2.0–5.3); p<0.001). However, side effects were more frequent in the intervention group (71.1% vs 23%; p<0.001).

Conclusions

Intranasal sufentanil was associated with more effective pain relief than oral/intravenous opioids in patients with traumatic injuries treated with coanalgesia. Intranasal sufentanil could be considered for the management of pain in patients with traumatic injuries associated with severe pain.

Trial registration number

NCT04137198
Comparing physical work exposures between men and women: findings from 65 281 workers in France
Authors: Wuytack Francesca , Evanoff Bradley , Dale Ann-Marie , Gilbert Fabien , Fadel Marc , Leclerc Annette , Descatha Alexis
Published: September 2023

Objectives

Musculoskeletal disorders (MSDs) are a leading cause of disability and sick leave among workers. Although MSDs are associated with physical exposures, there are gender differences in the prevalence and related disability. This study aimed to compare self-reported physical work exposures by gender for people within the same occupational group.

Methods

We used cross-sectional data from 65 281 asymptomatic workers aged 18–69 years from the CONSTANCES cohort study (France). We compared 27 physical exposures between men and women in the same occupational groups (‘Profession et Categorie Sociale’ group) using Mann-Whitney U tests.

Results

Men and women performing the same job often reported different levels of exposure. 38 of 365 occupational groups had a gender difference in reported exposure for 10 or more of 27 physical exposures, with men reporting higher exposures in 79% of these jobs. Women reported higher exposures in nursing and other healthcare professions. The probability that a random man had an exposure value higher than a random woman varied widely, from 8% to 92%, and was highly dependent on occupational groups and the specific exposure.

Conclusions

Men and women working in the same jobs reported different physical exposures for some jobs and some exposures. Further research should further define and explore these reported differences to improve prevention and research.