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At least 1,135 records · Page 63

Evaluating temperature dependent degradation mechanisms of silicon-graphite electrodes and the effect of fluoroethylene carbonate electrolyte additive

Understanding and overcoming the relatively high rate of degradation observed with silicon (Si)-based anodes in lithium (Li)-ion batteries (LiBs) is crucial for developing cells with increased energy density. The capacity fade of Si-graphite (Gr) electrodes in electrolytes with and without 10 wt% fluoroethylene carbonate (FEC) is explored during cycling at temperatures of 25, 45, and 70 °C, focusing on the complex degradation modes arising from loss of active material, loss of Li inventory, and cell resistance growth. We first show the common half-cell testing cannot be used at elevated temperatures as the performance of Li metal overshadows the behavior of Si-Gr electrode. Symmetric cell configuration successfully eliminates the effect of foreign materials, and reveals that while the presence of FEC provides better passivation of Si, it also results in greater parasitic reactions at 70 °C and faster capacity fade. The electrolyte without FEC, on the other hand, shows higher capacity retention at 70 °C. Finally, we propose a multi-step test protocol which can successfully deconvolute the information on loss of Li inventory and of active material. By combining half-cell and symmetric cell test methods, we demonstrate that loss of active material is reduced at higher temperatures and the dominant mechanisms of capacity loss for Si-Gr electrodes at elevated temperatures is loss of Li inventory.

25 ENERGY STORAGE↗

Efficient Screening of Bi–Metallic Electrocatalysts for Glycerol Valorization

Glycerol is a byproduct of biodiesel production and, as such, it is of limited economic value. By means of electrooxidation, glycerol can be used as a feedstock for scalable hydrogen production, in addition to conversion to value-added products. The development of novel and efficient catalytic electrode materials for the anodic side of the reaction is a key towards a hydrogen-based energy economy. In the present study, a computational screening protocol combining DFT, scaling relations, and microkinetic modeling allows for a rational selection of novel catalysts that can deliver efficient glycerol electrooxidation, low cost of production, and environmental sustainability. Activity and chemical selectivity towards hydrogen production on pure metal catalysts is discussed in terms of volcano-shaped plots. We find that the selectivity in the glycerol oxidation reaction is influenced by a different energy landscape when in the presence of water and best classified by a comparison of O—H and C—H bond-breaking barriers. In addition, we screened 3570 bi-metallic catalysts in the AB (L1 0 ) and A 3 B (L1 2 ) ordered structures for activity, stability, price, and toxicity. By filtering based on the criteria for toxicity, resistance to oxidation, miscibility, and price, we have identified 5 L1 0 structured catalysts (AgPd, AuPd, PtSb, CuPt, and AgPt) and 20 L1 2 catalysts (Ga 3 Ta, In 3 Ta, Ir 3 W, Ir 3 Mo, Cu 3 Pt, Ir 3 Ta, Ir 3 Re, Pd 3 Bi, Pd 3 Cu, Pd 3 W, Pd 3 Co, Pd 3 Sn, Pd 3 Mo, Pd 3 Ag, Pd 3 Ga, Pd 3 Ta, Au 3 Ru, Pd 3 In, Au 3 Ir, and Pd 3 Au) that are all predicted to show high activity. We also identify an additional 37 L1 0 and 92 L1 2 structured electrocatalysts with an anticipated medium-high activity.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Metal dissolution from first principles: Potential-dependent kinetics and charge transfer

Metal dissolution and its inverse process are integral to both corrosion and electrodeposition; however, many mechanistic details regarding the dissolution process are challenging to decipher. These include how ion dissolution kinetics and charge transfer are influenced by the competition between metal and solvent interactions under an electrode potential. Here, in this work, we introduce a computational framework based on density functional theory with grand-canonical treatment of electrons to directly predict the potential energy landscape for metal dissolution at a constant potential. Using aluminum as an example, we demonstrate that dissolution kinetics is governed by competing kinetics between two physical processes associated with metal–metal bond breaking and ion-migration within the electrical double layer, respectively. We identify a kinetic transition between regimes dominated by each of these processes and show that this transition depends on the operating electrode potential, among other key factors. It is further found that kinetics and thermodynamics of these processes can be described with a simple, one-parameter Marcus-theory-type model. Beyond offering new understanding of charge transfer during dissolution, our simulation protocol provides a recipe for directly predicting other important quantities in electrochemical reactions from first principles that are difficult to measure, such as the symmetry factor.

36 MATERIALS SCIENCE↗

Retrofitting of carbon-supported bimetallic Ni-based catalysts by phosphorization for hydrogen evolution reaction in acidic media

Commercial bimetallic Ni–Mo and Ni–Re electrocatalysts supported on advanced conductive carbon are high-performing materials for H 2 evolution reaction in alkaline medium, but they cannot be applied in acidic medium due to the rapid dissolution of metals. Here, in this work, we solve this issue by introducing a convenient phosphorization protocol to convert these commercial metallic electrocatalysts into the respective phosphides, rendering them stable under acidic conditions, and thus providing a pathway to electrocatalysts for water reduction in acidic electrolyte. The novel materials demonstrate high performance, as reflected by the measured low overpotentials, shallow Tafel slopes, fast kinetics, as well as excellent stability and durability. This work opens the possibility to expand the applicability of commercial supported metallic catalysts and electrocatalysts through retrofitting via phosphorization.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Achieving high rate performance in hybrid pristine-recycled cathodes using model-informed electrode designs

Direct recycling lithium-ion battery cathodes, a process that retains the engineered oxide structures from end-of-life materials, presents a cost-effective and energy-efficient alternative to other battery recycling methods. However, while direct-recycled cathodes have demonstrated performance comparable to that of pristine materials at low cycling rates, their high-rate performance remains uncertain. Morphology changes in cathode particles, a main mode of degradation, directly impact rate performance by limiting surface kinetics and solid-phase diffusion. If direct recycling processes do not sufficiently restore pristine-like morphologies, the recycled materials may retain structural defects that hinder high-rate performance. The present work uses a physics-based pseudo-2D model to simulate hybrid electrodes with pristine and artificially “aged/recycled” NMC materials to investigate potential impacts of incorporating performance-limited aged cathode materials into cells. The study highlights how differences in transport and kinetic properties can influence rate capabilities in mixed electrodes — particularly in high-loading cells in high-demand applications. However, model results also reveal a possible mitigation strategy via dual-layer electrode architectures with lower-performing materials positioned near the current collector. Simulations of 4.0 mAh cm −2 cells cycled at 4C using a dual-layer architecture provided approximately 5%–30% more capacity in constant-current protocols compared to homogeneously blended electrode architectures with the same loadings and mixed-material compositions. These findings highlight the importance of strategic electrode design in minimizing potential performance losses and facilitating the integration of recycled materials into high-performance batteries, advancing sustainable and cost-effective battery manufacturing.

25 ENERGY STORAGE↗

Exploratory analysis and performance prediction of big data transfer in High-performance Networks

Big data transfer in large-scale scientific and business applications is increasingly carried out over connections with guaranteed bandwidth provisioned in High-performance Networks (HPNs) via advance bandwidth reservation. Provisioning agents need to carefully schedule data transfer requests, compute network paths, and allocate appropriate bandwidths. Such reserved bandwidths, if not fully utilized, could be simply wasted due to the exclusive access during the approved time window, and cause extra overhead and complexity for resource management. This calls for accurate performance prediction to reserve bandwidths that match actual needs and avoid over-provisioning. We employ machine learning algorithms to predict big data transfer performance based on extensive performance measurements collected in the past several years from data transfer tests using different protocols and toolkits between various end sites on several real-life physical or emulated testbeds. We first analyze the performance patterns in response to a comprehensive list of parameters in end-host systems, network connections, and data transfer applications, which motivate the use of machine learning and also help us identify the effects of latent factors. We then propose threshold- and clustering-based methods to eliminate negative effects of latent factors in data preprocessing and build a robust performance predictor based on customized domain-oriented loss functions. The performance of the proposed methods is verified by extensive experiments using SVR and RFR as well as theoretical analysis of the general performance bound.

97 MATHEMATICS AND COMPUTING↗

Effect of Specimen Thickness on Fracture Toughness and Plane Stress to Plane Strain Transition in Medium-Density Polyethylene

This study investigates the effect of specimen thickness on fracture toughness and the transition from plane stress to plane strain conditions in Medium-Density Polyethylene (MDPE) using Single Edge Notched Bend (SENB) specimens. Three thickness groups (7.5 mm, 9.0 mm, and 12.1 mm) were tested following ASTM D5045 protocol. Conditional stress intensity factors (KQ) increased from 2.2 MPavm to 2.8 MPavm with increasing thickness, demonstrating significant size dependency. Confocal microscopy revealed a 34% reduction in maximum crack tip opening displacement (CTOD) from 0.478 mm to 0.314 mm as thickness increased, with plastic zone lateral extent decreasing by 32%. This quantitative evidence validates Irwin's theoretical prediction of plastic zone size transition from plane stress r_y˜ (1/2p) (K_I/s_y )^2 to plane strain conditions r_y˜ (1/6p) (K_I/s_y )^2.Scanning electron microscopy confirmed progressive suppression of shear lips and evolution from ductile tearing with extensive polymer chain drawing in thin specimens to localized crazing in thick specimens. Despite all specimens satisfying the ASTM D5045 criterion (B = 2.5(KQ/s_y )^2 ) ,residual shear lips persisted even in the thickest specimens, demonstrating that nominal size requirements alone are insufficient for achieving complete plane strain conditions in highly ductile polymers. The findings emphasize the necessity of integrated analytical and morphological assessment for valid fracture toughness characterization, with critical implications for life assessment and integrity prediction in thick-section polymer components such as natural gas distribution pipelines.

Medium-Density Polyethylene↗

Perspectives on the relationship between materials chemistry and roll-to-roll electrode manufacturing for high-energy lithium-ion batteries

As lithium-ion battery (LIB) active material and cell manufacturing costs continue to drop with wider adoption of electric vehicles, electrode and cell processing costs remain too high in terms of reaching the ultimate U.S. Department of Energy (DOE) cell cost target of $80/kWh. This paper primarily covers major materials chemistry advancements made over the last 10 years at Oak Ridge National Laboratory (ORNL) in the space of advanced manufacturing science for LIBs with the aim of simultaneously meeting the ultimate cost target, 500 Wh/kg gravimetric energy density, 10-15-min fast charge times, and 1000 deep discharge cycles. Aqeous electrode processing with a variety of active anode and cathode materials is now a standard procedure at the DOE Battery Manufacturing R&D Facility at ORNL (BMF), including the latest processes developed for Ni-rich cathodes. New results on cobalt-free LiNi 0.8 Fe 0.1 Al 0.1 O 2 (NFA 811) are also included and discussed in an electrode processing advantage context. In addition, colloidal processing advancements have been made for Si/C composite anodes for achieving >600 mA h/g capacities. Optimization of electrode coating parameters and drying protocols have been completed, which has elucidated how key processing variables need to be changed when parameters such as slurry solids loading, solvent type, and wet electrode thickness are changed. ORNL has also increased the line speeds at which thick cathodes can be processed using ultra-fast electron beam (EB) curing and substantially decreased formation cycling times to <1 day. Finally, key details of these advancements are discussed in the context of materials chemistry and process-property-performance relationships.

25 ENERGY STORAGE↗

Investigating explainable transfer learning for battery lifetime prediction under state transitions

Battery lifetime prediction at early cycles is crucial for researchers and manufacturers to examine product quality and promote technology development. Machine learning has been widely utilized to construct data-driven solutions for high-accuracy predictions. However, the internal mechanisms of batteries are sensitive to many factors, such as charging/discharging protocols, manufacturing/storage conditions, and usage patterns. These factors will induce state transitions, thereby decreasing the prediction accuracy of data-driven approaches. Transfer learning is a promising technique that overcomes this difficulty and achieves accurate predictions by jointly utilizing information from various sources. Hence, we develop two transfer learning methods, Bayesian Model Fusion and Weighted Orthogonal Matching Pursuit, to strategically combine prior knowledge with limited information from the target dataset to achieve superior prediction performance. From our results, our transfer learning methods reduce root-mean-squared error by 41% through adapting to the target domain. Furthermore, the transfer learning strategies identify the variations of impactful features across different sets of batteries and therefore disentangle the battery degradation mechanisms and the root cause of state transitions from the perspective of data mining. These findings suggest that the transfer learning strategies proposed in our work are capable of acquiring knowledge across multiple data sources for solving specialized issues.

25 ENERGY STORAGE↗

Retrofitting stoves with forced jets of primary air improves speed, emissions, and efficiency: Evidence from six types of biomass cookstoves

Incorporating jets of forced air into biomass cookstove combustion has been shown to potentially decrease harmful emissions, leading to a variety of designs in recent years. These have shown mixed success in terms of real-world performance, usability, and durability. The Jet-Flame forced draft retrofit accessory was recently developed to implement forced jets of primary air at a low cost into a wide range of types of cookstoves using a small 1.5-W fan housed in a low-cost cast iron body to be inserted beneath the fuel bed of a biomass cooking fire. This research sought to quantify the potential efficiency and emissions performance impacts of the Jet-Flame when installed in six different types of biomass cookstoves (three open or shielded fires and three rocket stoves) versus the natural draft performance of each. The effect of the operating fan voltage was also measured. A series of tests following a modified ISO 19867-1:2018 protocol were performed in the laboratory using the Aprovecho Laboratory Emissions Measurement System (LEMS) equipped with additional oxygen and temperature sensors. Results for each stove carefully tended with a single layer of sticks showed that the global average PM 2.5 reduction with the Jet-Flame was 89 % relative to the natural draft cases, with larger relative improvements seen in the most rudimentary stoves. CO was reduced by a global average of 74 %, reaching tier 4 or 5 for all stoves. Thermal efficiency was also improved by 34 % when calculated without taking into account the energy content of the remaining char (or 21 % with char), illustrating the value of burning char to provide cooking energy rather than leaving it unburned in the combustion chamber as is common in many natural draft stoves. Time to boil was also reduced by 8 %. In addition, adjusting the voltage of the jet-flame assisted in modulating firepower, improving the usability of the stove. These results indicate a strong potential for the Jet-Flame to help reduce emissions and fuel consumption in a wide range of cookstove designs at a relatively low cost or need for changes in behavior, fuel, or cooking device. The unit also provides enhanced usability in terms of ease of startup, cooking speed, low PM emissions at high power, and burning char. Additional studies are needed to measure performance in the field and under a variety of operational conditions.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

Robust hierarchical three dimensional nickel cobalt tungstate-MXene nanocomposite for high performance symmetric coin cell supercapacitors

Design of rational electrode materials with hierarchically structured pseudocapacitive materials has been identified as a successful strategy for revamping the electrochemical characteristics of supercapacitors. However, the simultaneous assurance of strong electrical conductivity and higher specific capacitance via a simple and cost-effective synthesis protocol is of concern today. In this work, Ni-CoWO 4 incorporated MXene nanocomposite has been successfully prepared by a facile hydrothermal process. The structure, morphology, stoichiometry, and chemical characterization of the prepared Ni-CoWO 4 , Ti 3 C 2 T x , and Ni-CoWO4@MXene nanocomposite were explored extensively. MXene demonstrated a layered 2D nanoscale structure whereas pure Ni-CoWO 4 was composed of spherical nanoparticles. The composite Ni-CoWO 4 @MXene revealed the morphology of spherical Ni-CoWO 4 nanoparticles decorated on MXene sheets. Furthermore, a series of electrochemical characterizations illustrated the contribution of the delightful morphology of Ni-CoWO 4 @MXene composite to excellent electrochemistry. The resultant Ni-CoWO 4 @MXene electrode exhibited high specific capacitance of 582 Fg -1 at 1 A g -1 and 93.5 % capacitance retention over the 10,000 cycles., which is higher than that of the individual counterparts Ni-CoWO 4 and MXene. The excellent electrochemical performance of Ni-CoWO 4 @MXene is attributed to the synergistic interaction between Ni-CoWO4 nanoparticles and nanolayered MXene that may create new active sites that enhanced the charge transfer, charge/discharge kinetics and the electrical conductivity. Significantly, the unique Ni-CoWO 4 @MXene supercapacitor prototype delivered a higher energy density of 85.7 Wh kg -1 and power density of 0.85 kW kg -1 establishing its superiority over other Cosingle bondW based supercapacitors. A theoretical study was also performed to correlate the improved charge storage capacity of the prepared Ni-CoWO 4 @MXene composite.

25 ENERGY STORAGE↗

Mechanically induced thermal runaway severity analysis for Li-ion batteries

Thermal runaway is one of the most important safety concerns in the deployment of Li-ion batteries. For this study, we developed a standardized single-side indentation test protocol to induce an internal short-circuit. Cell voltage, temperature, and applied compressive force are monitored as a function of time. Each cell is given an observed hazard severity (OHS in five categories) modified from the EUCAR table. Meanwhile a calculated hazard severity (CHS) is obtained solely based on temperature and voltage curves. The calculation formula considers the cell temperature, rate of temperature increase, cell capacity, state of charge (SOC), voltage drop, and voltage drop rate. Each term is assigned with an appropriate weight to scale the calculated score from 0 to100. This method was applied to >100 Li-ion batteries with various SOCs and chemistries, and their OHS and CHS were displayed against SOC. The purpose is to provide battery designers, manufacturers, and end-users a clear comparison of thermal runaway severity of different batteries.

25 ENERGY STORAGE↗

Mechanically induced thermal runaway severity analysis of Li-ion batteries and continuous energy release monitoring

The large-scale deployment of Li-ion batteries in stationary energy storage and electrical vehicle applications demands a strong focus on safety, particularly on the thermal runaway risk and severity evaluation. A standardized single-side mechanical indentation test protocol was developed to induce an internal short-circuit (ISC) and evaluate cells' thermal runaway severity at different state of charge (SOC). The observed hazard severity (OHS in five categories) and evaluated scores in this work have a comprehensive consideration of each cell's capacity, initial voltage, SOC, temperature and voltage change, allowing a better evaluation of the cells' thermal runaway potential. This method was applied to about 200 Li-ion batteries in order to build an extensive thermal runaway database covering various SOCs, capacities and chemistries. In this study, we monitored the transitions of stored electrochemical energy and applied mechanical energy into both thermal energy and acoustic emissions (AE). The surface temperature and mechanical failures were monitored by infrared imaging and AE to capture critical events within battery cells throughout the mechanical indentation tests. Furthermore, the initial temperature maps can predict two types of follow-up events: thermal runaway or gradual heat release via conduction. Analyzing each cell's severity, AEs, and leveraging the evolving database offer insights into predicting occurrences of thermal runaway. The test method, thermal runaway severity evaluation and prediction, and the corresponding database provide battery designers, manufacturers, and end-users a clear overview of Li-ion batteries' thermal runaway potential under mechanical abuse, advancing the safety design of Li-ion batteries.

Acoustic emission↗

Towards generic memory forensic framework for programmable logic controllers

A Programmable Logic Controller (PLC) is a microprocessor-based controller that is used to automate physical processes in critical infrastructure and various other industries and manufacturing sectors. Initially, PLCs were completely isolated from the Internet, and cyber security was not incorporated at the time of development. The introduction of industry 4.0 and the evolution of ICS systems to communicate over public IP addresses from the Internet enhanced productivity and efficiency, but Internet connectivity exposed the systems and their vulnerabilities, which led to an increase in cyber attacks. When a system is sabotaged/compromised, security analysts need to get to the root cause of the attack as quickly as possible to recover the system. To do so, memory forensic analysis is critical to provide a unique insight into the run-time memory activities and extract a reliable source of evidence. In this paper, we analyze the memory structure of the Schneider Electric Modicon M221 PLC. To build a memory profile, we reverse engineer the communication protocol and conduct differential analysis to gain knowledge about the structure of the memory and the low-level representation of control logic instructions. We then identify dynamic and static memory regions by modifying different project fields and conducting differential analysis, which allows us to identify boundaries of critical memory structures and extract important forensic artifacts that can be found in the memory. The Python implementation of the memory profile can help reduce the time and effort required for manual analysis in case of cyber incident or system failure.

97 MATHEMATICS AND COMPUTING↗

Development and application of a machine learning based multi-objective optimization workflow for CO 2 -EOR projects

Carbon dioxide-Enhanced Oil Recovery (CO 2 -EOR) is known as one of techniques for hydrocarbon production improvement as wells as an important candidate to reduce greenhouse gas emissions. Thus, an ideal development strategy for a CO 2 -EOR project would consider multiple objectives including to maximize oil recovery, CO 2 storage volume and project economic outcomes. This work proposes a robust computational framework that couples artificial neural network (ANN) and multi-objective optimizers to optimize the aforementioned objectives in CO 2 -EOR processes simultaneously. Expert ANN systems are trained and employed as surrogate models of the high-fidelity compositional simulator in the optimization workflow. The robustness of the development optimization protocol is confirmed via a synthetic injection-pattern-base case study. Afterward a field implementation to Morrow-B formation to optimize the tertiary recovery stage of the field development is discussed. This work compares the optimum solution found using an aggregate objective function and the solution repository (Pareto front) generated by the multi-objective optimization process. The comparison indicates the existence of potential multi-solutions satisfying certain criteria in a CO 2 -EOR project designing, which cannot be found using traditional weighted sum method. The optimization results provide significant insight into the decision-making process of CO 2 -EOR project when multiple objective functions are considered.

02 PETROLEUM↗

Non-catalytic gas phase NO oxidation in the presence of decane

In this study, experiments without a catalyst revealed that NO was oxidized in a diesel exhaust gas phase mixture due to the presence of n -C 10 H 22 (decane). This reaction was observed to occur following the low temperature oxidation catalyst test protocol (LTC-D) defined by U.S. DRIVE. The purpose of LTC-D conditions is to simulate an aftertreatment diesel combustion gas mixture in order to test candidate catalyst materials. 100% NO conversion was observed, without a catalyst, after beginning to react at 330 °C accompanied by consumption of decane. After experiments that isolated hydrocarbons, ethylene was also observed to facilitate NO oxidation to a lesser degree (>470 °C). Density functional theory (DFT) calculations were conducted to investigate thermodynamically-possible initiating elementary steps during n -C 10 H 22 consumption and NO oxidation. Two feasible intermediate radicals to oxidize NO to NO 2 are ·C 10 H 21 O 2 and ·HO

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Gasoline fuels properties for multi-mode operation – Observations in a GDI and the CFR engine

The combustion behavior of five full boiling range RON98 gasoline blends was evaluated for multi-mode operation in a GDI and the CFR octane rating engine. The GDI engine tests were conducted with stoichiometric air-fuel ratio in spark-ignition (SI), and with air-diluted homogeneous charge compression ignition (HCCI) mode. In the CFR engine, the knocking combustion was analyzed under standard RON testing conditions at both peak knocking lambda and stoichiometric air-fuel ratios, whereas compression ignited operation was characterized by utilizing the HCCI number test protocol. Disparate knock limited SI and HCCI combustion behavior was observed for the test fuels, despite four of the fuels having the same RON and octane sensitivity. It was found that knock-limited combustion phasing in the GDI engine did not align well with the RON. However, a detailed comparison of knock-limited SI operation in the GDI and CFR engine revealed that a more appropriate effective RON based on a common knock intensity metric (MAPO) and stoichiometric air-fuel ratio resulted in comparable knock characterization between the two engine platforms. Furthermore, the critical intake air temperature and the critical compression ratio were proposed to characterize knock-limited SI operation, while the minimum intake air heating and compression ratio were used to define a fuel's autoignition propensity for compression ignition operation in the GDI and CFR engine, respectively. Finally, each fuel's characteristic compression ratio needed to obtain knock-limited SI (KLSI) and HCCI operation was used to calculate an effective multi-mode octane number (MM-ON) based on the primary reference fuel blends.

33 ADVANCED PROPULSION SYSTEMS↗