Industrial Processes Research Articles

Surrogate gradient methods for data-driven foundry energy consumption optimization

In many industrial applications, data-driven models are more and more commonly employed as an alternative to classical analytical descriptions or simulations. In particular, such models are often used to predict the outcome of an industrial process with respect to specific quality characteristics from both observed process parameters and control variables. A major step in proceeding from purely predictive to prescriptive analytics, i.e., towards leveraging data-driven models for process optimization, consists of, for given process parameters, determining control variable values such that the output quality improves according to the process model. This task naturally leads to a constrained optimization problem for data-driven prediction algorithms. In many cases, however, the best available models suffer from a lack of regularity: methods such as gradient boosting or random forests are generally non-differentiable and might even exhibit discontinuities. The optimization of these models would therefore require the use of derivative-free techniques. Here, we discuss the use of alternative, independently trained differentiable machine learning models as a surrogate during the optimization procedure. While these alternatives are generally less accurate representations of the actual process, the possibility of employing derivative-based optimization methods provides major advantages in terms of computational performance. Using classical benchmarks as well as a real-world dataset obtained from an industrial environment, we demonstrate that these advantages can outweigh the additional model error, especially in real-time applications.

Carbon footprint tracing and pattern recognition framework based on visual analytics

With growing concerns about global warming, industrial carbon footprints have garnered increased attention due to the energy-intensive and uninterrupted operation of industrial equipment. Fine-grained modeling and visual analytics of industrial carbon footprints can reveal the mechanisms behind the formation and evolution of carbon chains. However, the mechanisms underlying industrial carbon emissions remain unclear, leading to a lack of accuracy and specificity in current carbon quantification models. To address these gaps, we developed a comprehensive quantitative model that considers specific pathways involved in industrial processes, providing more accurate estimations of carbon emissions. We also designed an innovative visual analytical framework that uncovers implicit patterns and spatiotemporal distributions of industrial carbon footprints. By comparing our approach with state-of-the-art studies, we validated the superiority of our method in terms of its intuitiveness and interactivity. Empirical studies revealed potential emission patterns and spatiotemporal dynamics that traditional studies could not identify. We identified four consistent patterns in industrial carbon emissions: normal, high-emission, low-emission, and dedicated patterns. Our findings also led to optimization suggestions for different emission patterns, highlighting the system’s capability in extracting valuable insights from workshop carbon emission data. Our research showcases a unified visual analytical approach that supports exploratory analysis, and we believe it will uncover implicit knowledge within industrial carbon data, providing valuable insights for optimization.

Teachers’ Perception of Urban Noise in The Classroom: Case Study in Mukah, Sarawak

Environmental noise is an unwanted sound created by urbanisation and industrialisation processes, which has been linked to problems of teachers in the school such as lack of concentration, annoyance, speech interference and low performances. The impact of noise in the classroom during the teaching session can cause increased stress and fatigue to teachers. This study aims to identify the noise factor in the school and the impacts of urban noise on teachers during the teaching session through subjective evaluation. In the present study, a questionnaire survey was distributed to the teachers at three secondary schools in Mukah, Sarawak. The developed questionnaire was evaluated by the experts and pilot study was carried out prior to the actual survey. A total of 204 respondents from secondary schools named as School A, School B and School C were participated in this study. The questionnaires were sent to all teachers in three schools in the form of Google Forms and sent by using the WhatsApp application. The main factor of environmental noise that affects teachers from all schools studied came from transportation. Most of the teachers agreed that they need to raise their voice and walk around inside the classroom during the class in order to make sure students can hear their message clearly

Secondary Network Formation in Epoxidized Natural Rubber with Alternative Curatives.

Epoxidized natural rubber (ENR) offers a unique combination of strength, sustainability, and versatile structure, making it a good candidate for creating sacrificial and reformable bonds via secondary curatives. These curatives should be simple and compatible with other rubber ingredients and industrial mixing processes. Although many alternative curatives, including zinc chloride, zinc dimethacrylate (ZDMA), and sebacic acid (SA), have been proven to be successful, they have never been compared in the same compound. Moreover, the effectiveness of these alternative curatives on network formation, including their interactions with the other ingredients of a rubber compound, such as fillers, coupling agents, and traditional curatives, has not been fully studied yet. Based on the current study, these secondary curatives alone cannot provide a sufficient level of vulcanization. However, adding ZDMA or SA together with sulfur curatives allowed improving the tensile properties and showed microlevel self-healing behavior during cyclic loading. The addition of 10 phr (parts per hundred rubber) ZnCl2 created weak, short, and rigid bonds in ENR detectable by simple Payne measurements. However, that led to active interaction with all compounding ingredients and deterioration of the physical properties. Reducing the ZnCl2 load to 1 phr allowed full recovery of stress after the cyclic test and fair tensile strength of the compound.

Industrial process fault detection based on dynamic kernel principal component analysis combined with weighted structural difference

AbstractThe practical application of traditional data‐driven techniques for process monitoring encounters significant challenges due to the inherent nonlinear and dynamic nature of most industrial processes. Aiming at the problem of nonlinear dynamic process monitoring, a novel fault detection method based on dynamic kernel principal component analysis combined with weighted structural difference (DKPCA‐WSD) is proposed in this paper. Initially, the proposed method leverages a sophisticated nonlinear transformation to project the augmented matrix of the original input data into a high‐dimensional feature space, thereby facilitating the establishment of a DKPCA model. Subsequently, the WSD statistic is computed, utilizing a widely known sliding window technique, to quantify the mean and standard deviation differences across data structures. Ultimately, the WSD statistic is utilized for fault detection, completing the process monitoring task. By integrating the capability of DKPCA to capture nonlinear dynamic characteristics with the effectiveness of the WSD statistic in mitigating the impact of non‐Gaussian data distributions, DKPCA‐WSD significantly enhances the monitoring performance of traditional DKPCA in nonlinear dynamic processes. The proposed method is evaluated through a numerical case exhibiting nonlinear dynamic behaviors and a simulation model of a continuous stirred tank reactor. A comparative analysis with conventional methods, including principal component analysis (PCA), dynamic principal component analysis, KPCA, PCA similarity factor (SPCA), DKPCA, and moving window KPCA (MWKPCA), demonstrates that DKPCA‐WSD outperforms traditional fault detection techniques in nonlinear dynamic processes, offering a substantial improvement in monitoring performance.

Optimization and prediction of dye adsorption utilising cross-linked chitosan-activated charcoal: Response Surface Methodology and machine learning

Water pollution poses a significant environmental threat due to the discharge of organic dyes from industrial processes. In this study, we investigated a novel adsorptive composite material for removing MB dye from water and applied new models for optimizing and predicting adsorption efficiency. The cross-linked chitosan-activated charcoal composite material was synthesized using a single-step process and characterized by analytical techniques including FTIR, XPS, BET, SEM, TGA, and zeta potential, which demonstrated its remarkable physicochemical properties. Batch adsorption experiments showed a direct correlation between increased adsorbent dosage and enhanced MB dye adsorption, with an optimal dosage of 0.4 g/25 mL. Additionally, pH-dependent studies revealed excellent performance at lower pH levels, achieving up to 95 % adsorption. The composite reached equilibrium within the first 360 min of contact time and maintained a stable 99 % adsorption efficiency afterward. These results were confirmed by an applied optimization RSM model. Additionally, adsorption isotherms and kinetics were thoroughly analyzed by fitting them into various models, revealing a maximum dye adsorption capacity of 625 mg/g. ML models, such as RF, XGBoost and stacking, were used to predict adsorption efficiency, with a stacked model combining RF and XGBoost showing the highest predictive accuracy. Variable importance plots indicated that contact time was the most significant factor influencing adsorption, followed by dosage and pH, in line with RSM findings. This research highlights the eco-friendliness and cost-effectiveness of the composite material in addressing water pollution challenges. Integrating ML models and RSM enhances the predictability and optimization of the adsorption process, offering valuable insights for mitigating the impact of organic dyes on aquatic ecosystems and public health.

The retention factors and partial molar volumes of cycloartenyl ferulate at infinite dilution in supercritical carbon dioxide: Measurements and correlation

The retention factors and infinite dilution partial molar volumes of a solute in supercritical fluid are crucial for understanding solute–solvent interactions, analyzing pressure effects on chemical reactions, and optimizing industrial processes. Despite their importance, there is a notable scarcity of available data on retention factors and infinite dilution partial molar volumes in supercritical fluid systems. This study presents the first experimental data on the retention factors and partial molar volumes of cycloartenyl ferulate at infinite dilution in supercritical carbon dioxide. The retention factors were measured across temperatures ranging from 308.15 to 353.15 K and pressures of 8.20 to 31.03 MPa using supercritical fluid chromatography. The infinite dilution partial molar volumes were determined by taking the partial derivative of the retention factors with respect to the density of carbon dioxide. The obtained infinite dilution partial molar volumes range from −1.64 × 10−2 to 4.10 × 10−4 m3/mol, with large negative values observed near the critical point of carbon dioxide, indicating strong solvent–solute interactions. In this study, a new correlation model successfully predicts the experimental infinite dilution partial molar volumes with an average absolute relative deviation of 2.3 % over 86 data points.

Breaking bread: Assessment of household bread waste incidence and behavioural drivers

Bread, the most consumed floury product in the world, is one of the most wasted food products across the food supply chain. Yet, there is a paucity of research examining the magnitude of bread waste and behavioural drivers associated with it at the household level. This study analyses the incidence, behavioural drivers and disposal pathways of bread waste using a sample of 1462 households in Australia and a 7-day electronic diary method. Behavioural drivers related to bread waste were modelled using logistic regression models. The findings suggest that bread rolls, sliced bread and flat bread are the three most wasted bread products in Australian households. Higher grocery shopping frequency, ‘just-in-case’ buying, family households with young children and takeaway meal ordering are associated with higher bread waste. Bread disposal pathway analysis revealed that 55% of bread and bakery products are discarded through general waste bins, highlighting the immense potential of utilising bread waste in industrial processes such as biofuel production. Addressing bread and bakery waste can significantly contribute to achieving the SDG12.3 goal of halving the per capita food waste by 2030.

Assembly and characterization of an optical fiber probe for use in a portable Raman spectrometer

This study describes the assembly and testing of an optical fiber Raman probe for use in a portable spectrometer using a 785 nm laser. The Raman probe was tested on edible vegetable oils (extra virgin olive, canola, sunflower, corn, and soybean), with the distal end of the probe immersed in each sample. The spectra of a dipyrone tablet and a mothball were also obtained. The silica peaks were subtracted by taking a spectrum with the probe tip immersed in water. The spectra of the samples exhibited characteristic peaks from vibrations of unsaturated fatty acids between 900 and 1800 cm−1, as well as characteristic peaks of silica from 600 to 1200 cm−1. The spectra of the samples after subtraction of the silica spectrum (probe in water) showed characteristic peaks without alterations in position or relative intensities compared without the probe. Exploratory analysis by principal component analysis was able to identify peaks of the constituent fatty acids in the oils and associate them with the oil type. A classification model based on partial least squares regression allowed for the classification of oil spectra with an accuracy of 77%. This study demonstrated the feasibility of coupling a probe to a portable Raman spectrometer with the goal of monitoring and controlling industrial processes.

Enhancing Efficiency and Selectivity of Electrochemical Regeneration of NADH By Dynamic Operation

The demand for the reduced nicotinamide adenine dinucleotide (1,4-NADH) and reduced nicotinamide adenine dinucleotide phosphate 1,4-NAD(P)H in industrial processes, particularly in the pharmaceutical sector, necessitates their continuous regeneration to mitigate cost implications [1]. In this respect, direct electrochemical cofactor regeneration is a promising alternative, but challenges in achieving optimal selectivity have hindered its widespread industrial integration.This study focuses on advancing the efficiency and selectivity of electrochemical NADH regeneration through dynamic potential inputs, inspired by recent findings demonstrating improved selectivity using dynamic potential inputs instead of steady-state conditions [2]. Various dynamic, pulsed potential profiles are tested and compared with steady-state conditions, revealing distinct advantages in selectivity. Nicotinamide adenine dinucleotide reduction reaction is then coupled with the enzymatic conversion of a substrate (cyclohexenone) to a product (cyclohexanone). In this electroenzymatic biotransformation, 1,4-NADH formed electrochemically is utilized as a second substrate of enzyme enoate reductase. The success of this coupling is evident in the observed formation of the desired product. This research not only contributes to the fundamental understanding of electrochemical co-factor regeneration but also demonstrates its practical application. By combining the advantages of electrochemical and enzymatic reactions, we pave the way for more sustainable and economically viable industrial processes. The presented results mark a significant step towards harnessing the full potential of electrochemical regeneration in facilitating enzymatic transformations with enhanced efficiency and selectivity through a pulsed dynamic operation.

Simulating the Effects of Ammonia Crossover and Alternative Ligands in Thermally Regenerative Flow Batteries

There is a significant amount of low-temperature heat (< 100 °C) available globally from various sources such as geothermal energy, industrial processes, and thermal power plants. Electrochemical and membrane-based methods for harnessing this heat are becoming of greater interest due to their ability to utilize the low temperature energy sources and because they can be sized modularly to fit the power, energy, and spatial requirements of the application. The all-aqueous thermally regenerative ammonia battery (Cuaq-TRAB) is a leading technology in the waste heat to electrical power production space due to recent results showing operational power densities of 25 mW cm-2 with an estimated thermal energy efficiency of 7%, outperforming most technologies in this space. Previous research on the Cuaq-TRAB has shown that the battery suffers from the crossover of ammonia through the membrane which adversely impacts battery performance.To investigate these impacts, we developed a simple numerical model that simulates discharge curves using one fitting parameter to account for parasitic crossover instead of modeling species transport through the membrane. The model was able to replicate experimental data from Cuaq-TRABs with different membrane materials and for multiple applied current densities with < 5% error for average power and energy capacity. Results showed that there is a 20% loss in Cuaq-TRAB energy capacity due to ammonia crossover when compared to a discharge with no parasitic crossover losses. The model was then extended to demonstrate the impact of different changes to the electrolyte chemistry on the resultant power and energy capacities for Cuaq-TRABs. Using this model, we estimate that if chloride was the ligand for the positive electrolyte instead of bromide, it would decrease the power and energy capacity of the battery by 30%. However, considering that an economic analysis showed that chloride is 80% cheaper than bromide, it may be beneficial to consider the use of chloride ligands in this technology despite its worse electrochemical performance.

High-Performance Humidity Sensor Capable for Monitoring Low-Moisture Levels in Energy Industries

Humidity sensors are extensively used in industrial processing and environmental control. There are two categories of humidity sensors: High-humidity (relative humidity) measurement and Low-humidity (moisture) measurement. The proper units for low-humidity measurement are dew point and parts per million in volume (ppmv) or parts per billion in volume (ppbv). It is very important to monitor moisture levels in natural gas during processing and transportation in pipelines. It is of critical importance to monitor moisture levels in lithium-ion battery manufacturing. In these cases, the moisture levels are limited to be less than -50°C dew point or 40 ppmv. The conventional relative humidity sensors cannot be used because of their low sensitivity. The dew point sensors on the market are polymer-based and gamma-phase-alumina based. The polymer-based sensors have low sensitivity, which can only accurately detect moisture levels of -60°C dew point and higher. The gamma-phase alumina sensors suffer from long-term drift due to phase change.We present novel high-performance humidity sensors based on porous alpha-phase alumina and silicon dioxide nanostructures, which are capable of detecting moisture levels as low as -100°C dew point or 13 ppbv with excellent long-term stability. The porous alpha-phase alumina films were produced by anodic-spark-deposition on aluminum substrates in which electric sparks were produced due to high-voltage anodization (>120V). Local high-temperature (about 2200°C) at the points of sparks converted the amorphous alumina into alpha-phase alumina (Sapphire). It provides an extremely stable structure for humidity sensors. Hydrophilic silicon dioxide films deposited by atomic layer deposition was used as sensing materials. The sensors’ sensitivity, temperature coefficient, response speed, and long-term stability were studied in details. The sensors have great promise for monitoring moisture levels in natural gas industry and lithium-ion battery manufacturing.

Measuring Charge Selective Contacts on Semiconducting Photocatalyst Particles

Water splitting could replace steam-methane reforming, a carbon intensive process, to generate hydrogen for energy storage, fertilizer production, and many other industrial processes. Photoelectrochemical (PEC) overall water splitting (OWS), for example, produces hydrogen from water, without wires or applied voltages, via a clean and renewable energy source, sunlight. To do so, PEC particles or photocatalysts (PC) must absorb light, and selectively collect the generated carriers, either electrons or holes, at specific sites which drive reduction or oxidation reactions, respectively. While theoretical solar to hydrogen (STH) efficiencies are at least 18%, state-of-the-art PCs are below 3%. Despite the extreme efforts screening and tuning semiconductor photocatalysts, a thorough understanding and control of electronic properties of the semiconductor-electrocatalysts (sem|cat) interface is limited.Here, we use advanced electrochemical methods (dual-working electrode), scanning probe microscopy (conductive-AFM) and ambient-pressure XPS to measure and understand the origins of the electrochemical driving force (gradient in electrochemical potential) developed at complimentary electrocatalysts on photocatalyst particles. We use the dual-working electrode geometry on a high-quantum-yield wide-bandgap perovskite oxide, SrTiO3, to demonstrate the adaptive nature of electrocatalysts, Pt and CoOx, as they accumulate electrons and holes respectively. The cooperativity of half-reaction kinetics and adaptive sem|cat barrier height will be demonstrated along with the influence of crystal termination, which we argue is commonly overemphasized. Finally, a new contactless method of measuring the photovoltage at chemically distinct catalysts sites will be presented. Measured photovoltage induced shifts to element specific core levels in ambient pressure XP spectra in the dark and under illumination directly probe interfacial mechanisms operando. In sum, the introduced techniques and their findings demonstrate the underlying mechanisms and the degree that they play, enabling directed research of photocatalyst systems under one dominant engineering condition, charge selective Figure 1

Steric Effects of Tetraalkylammonium Hydroxides on Nafion in Adiponitrile Production

Over the last two decades, the chemical industry has witnessed a 43% increase in greenhouse gas emissions, with organic chemicals contributing to 75% of the total chemical production [1]. While organic electrosynthesis is a promising method for reducing the environmental impact of chemical manufacturing, advancing this technology faces significant challenges due to the limited viability of membranes used in divided electrochemical reactors. Despite the need for suitable membranes for electrosynthesis, only a few studies have explored the use of ion-conducting membranes for electro-organic reactions [2-7]. In this study, we aim to provide additional insights into the role of membranes in electrosynthesis and their behavior when exposed to electrolytes containing organic components.We selected adiponitrile electrosynthesis, one of the largest industrial electro-organic processes, as a model reaction [2,3]. We used Nafion as a model membrane material. Electrolytes used in adiponitrile electrosynthesis contain tetraalkylammonium (TAA) ions to enhance the selectivity of electro-organic reactions. Here, we present a systematic study exploring how TAA ions impact Nafion's structure and transport properties. We exposed Nafion membranes to solutions with varying concentrations and sizes of TAA ions, including tetramethyl (TMA), tetraethyl (TEA), tetrapropyl (TPA), and tetrabutyl (TBA). We quantified TAA ion sorption in the membranes using ATR-FTIR. The results reveal that TAA ion sorption saturates when the solution concentration exceeds 0.02 M. Compared to the conductivity of Nafion equilibrated in pure water (9.8 x 10-2 S/cm), the membrane conductivity after exposure to TAA solutions exhibits a substantial decline, decreasing by up to 4 orders of magnitude. We performed small-angle X-ray scattering experiments to correlate the conductivity results with structural changes in Nafion. The measurements revealed that the spacing between Nafion's ion-conducting domains decreases as the TAA ion concentration increases. For example, when membranes are exposed to 1.0 M TBA solutions, the domain spacing drops from 4.9 nm to 3.76 nm compared to membranes equilibrated in pure water. Consequently, we infer those organic ions reduce the size of ion-conducting domains due to the diffusion of water from the membranes into the surrounding electrolyte, resulting in decreased conductivity. These results represent an important step in elucidating structure-property relationships in ion-conducting membranes used for organic electrosynthesis.Reference[1] 2022. Our Risks for Infectious Diseases Is Increasing Because of Climate Change. CDC:NCEZID [updated 2022 August 02]. https://www.cdc.gov/ncezid/what-we-do/climate-change-and-infectious-diseases/index.html.[2] N. Tanbouza. 2020 J. iScience. 23 101720[3] Suryanto, B. H. R.; Kristianto, H. A.; Yi, Z. "Electrosynthesis: An overview of green chemistry.” Current Opinion in Green and Sustainable Chemistry 16 (2019): 28-34.[4] Baizer, M.; Lust, E.; Williamson, S.E.; Connor, R.F. "Electrochemical Synthesis of Adiponitrile." Industrial & Engineering Chemistry Process Design and Development, 1966, 5 (1), 56–62.[5] D. E. Danly 1984 J. Electrochem. Soc. 131 435C[6] Blanco, D.; Prasad, P.; Dunningan, K.; Modestino, M. React. Chem. Eng., 2020, 5, 136[7] Katzenberg, A.; Angulo, A.; Kusoglu, A.; Modestino, M. Macromolecules 2021, 54, 5187−5195 Figure 1

(Invited) Plasmon-Driven Nitrogen Photofixation

Ammonia is one of the most important chemicals as well as an efficient energy carrier. The nitrogen element is indispensable for nearly all living species on the earth. However, dinitrogen molecules that are abundant in the atmosphere cannot be used directly by living species. They need to be converted into ammonia or nitrate for use. Such conversion is known as nitrogen fixation. Because natural nitrogen fixation is insufficient for sustaining all population on the earth, the industrial Haber-Bosch process has been developed for producing ammonia from dinitrogen and dihydrogen. The process consumes a large amount of energy and generates a large amount of carbon dioxide. Photocatalytic nitrogen fixation can operate under mild conditions and uses solar energy and water. It offers an intriguing alternative approach for the conversion of atmospheric dinitrogen into ammonia.Plasmonic metal nanocrystals can effectively absorb light and efficiently generate hot charge carriers. Compared to traditional interband excitation in semiconductors, plasmon excitation offers a new approach for generating hot charge carriers, which can thereafter be utilized for various physical and chemical processes. The use of plasmonic hot charge carriers for photocatalysis has become a hot research topic in the recent years. Plasmonic hot charge carriers can not only enhance the reaction yield and selectivity, but also introduce new reaction pathways. Moreover, the plasmon resonance wavelengths of various nanoparticles can be synthetically adjusted over a broad range that surpasses the solar spectrum.We have demonstrated the powerfulness of localized plasmon resonance on driving photocatalytic reduction of atmospheric nitrogen, where plasmonic hot electrons play an essential role. First, Au nanoparticles are anchored on ultrathin titania nanosheets with oxygen vacancies. The oxygen vacancies chemisorb and activate nitrogen molecules, which are subsequently reduced to ammonia by plasmonic hot electrons generated from the Au nanoparticles. Our synthesized all-inorganic hybrid structures mimic the function of biological nitrogenase enzymes, where the plasmonic Au nanoparticles play the role of the Fe proteins and the titania nanosheets play the role of the MoFe proteins. Second, we have extended the biomimetic idea into another popular semiconductor. A plasmonic hybrid catalyst is produced by uniformly embedding Au nanoparticles in the mesopores of nitrogen-deficient hollow carbon nitride spheres. The carbon nitride spheres possess abundant nitrogen vacancies, which serve as the sites for nitrogen chemisorption and activation, and capture photoexcited electrons stemmed from the carbon nitride spheres and the plasmon resonance of the embedded Au nanoparticles for the efficient reduction of the adsorbed nitrogen molecules to ammonia. Third, when metal-semiconductor hybrid nanostructures are designed as photocatalysts for nitrogen fixation, the introduction of a Schottky barrier at the junction is unavoidable. This Schottky barrier will undoubtedly lower the utilization efficiency of plasmonic charge carriers because only plasmonic hot charge carriers with sufficient energies and appropriate momenta can overcome the barrier and inject into the semiconductor. In addition, noble metals are well-known to be expensive. We have synthesized different metal oxide nanoparticles that are degenerately doped and thus exhibit strong plasmon resonance and demonstrated Schottky-barrier-free plasmonic photocatalysts for nitrogen fixation. The excitation of the plasmon resonance in these metal oxide nanoparticles can efficiently generate hot charge carriers. Because of the abundance of defects in these degenerately doped semiconductor nanoparticles, the photogenerated hot charge carriers can rapidly migrate to the defect sites and get trapped there. In this way, the lifetime of the plasmonic hot charge carriers is Moreover, these metal oxide nanoparticles possess active sites for adsorbing and activating dinitrogen molecules. Therefore, they function in a “one-stone-two-birds” manner. As a result, a record-high solar-to-chemical energy conversion efficiency has been achieved.

MFe2O4 (M: Cu, Ni)/SnS2magnetic Core-Shell Heterostructure for Photocatalytic H2 Generation and Environmental Remediation

Overgrowing demand for sustainable energy sources and environmental remediation has driven extensive research in the field of photocatalysis. This study presents a hydrothermal synthesis of a novel MFe2O4 (M: Cu, Ni)/SnS2 magnetic core-shell heterostructure. The engineered heterostructure combines the unique properties of MFe2O4 (M: Cu, Ni) magnetic crystal as a core with SnS2 nanosheets as a shell, aiming to exploit type-2 heterojunction properties to improve effects for improved photocatalytic performance. The structural chemistry, phase formation, and crystal orientation of the prepared material are investigated using various analytical techniques, including X-ray diffraction (XRD), transmission electron microscopy (TEM), Raman, and FTIR Spectroscopy. FE-SEM analysis reveals a well-defined core-shell morphology with intimate interfaces between MFe2O4 (M: Cu, Ni) crystals and SnS2 nanosheets. Photocatalytic efficiency is assessed through the industrial dye degradation process. The influence of the heterostructure's structural features on the photocatalytic activity is explored through density function theory, emphasizing the role of the core-shell interface and the magnetic properties of MFe2O4 (M: Cu, Ni) cores. The chemistry between the magnetic core and the shell in the heterostructure is demonstrated to be crucial for optimizing charge carrier dynamics and promoting surface reactions. Photocatalytic H2 generation under artificial simulated sunlight irradiation in the presence of MFe2O4 (M: Cu, Ni)/SnS2 core-shell heterostructure is demonstrated. This study not only presents a novel MFe2O4 (M: Cu, Ni)/SnS2 heterostructure for photocatalytic H2 generation but also provides valuable insights into the intricate relationship between structural chemistry and photocatalytic efficiency. The engineered heterostructure shows promising potential for applications in sustainable energy production and environmental remediation, contributing to the ongoing efforts towards a cleaner and more sustainable future.

Towards Rational Design of Battery Formation Protocols: From Electrochemical Modeling to Factory Deployment

In battery manufacturing, the formation process is both paramount and problematic. It is paramount since all batteries undergo formation charging and aging steps to build a resilient solid electrolyte interphase (SEI) and to screen for defects. It is problematic because the formation process is expensive to operate, remains a major source of factory energy demand, requires larger factory footprints, and takes an order of magnitude longer than nearly every other manufacturing step. Despite the centrality of the formation process in battery manufacturing, steps taken to optimizing formation protocols remain ad-hoc in the absence of design principles and physical models.We highlight recent progress in developing a principled understanding of the SEI formation process applied towards industrial battery formation process (i.e. in the context of a full cell). We review a reduced-order electrochemical model of the formation process that captures first cycle charge dynamics and subsequent cycling and aging degradation behavior [1]. The model predicts measurable quantities such as first cycle efficiency (FCE) and irreversible thickness growth under multiple formation protocols. We discuss opportunities to integrate the electrochemical formation model with differential voltage analysis (DVA) to visualize the connection between first cycle charge dynamics from the perspective of shifting electrode stoichiometries [2] and comment on applications towards physics-informed battery lifetime prediction [3]. We finally share recent data elucidating the effect of formation temperature, pressure, and protocol on cycle life.

Leveraging Flow-Assisted Electrochemistry Toward Decarbonization of Cement Manufacturing

In an effort to decarbonize the global economy by 50% in 2030 and achieve net-zero carbon emissions by 2050, there is a dire need to electrify industrial processes [1]. Approximately 8% of global carbon emissions comes from cement production [2]. Considering the rising demand for cement due to infrastructure expansion, it is critical to develop new concepts for the decarbonization of cement production. With this goal in mind, our work explores the effectiveness of flow-assisted cement electrolysis as a replacement to conventional fossil fuel based high-temperature kilns in cement manufacturing plants [3].In this study, we employ a scalable 3-channel flow-reactor with acidic anolyte, basic catholyte, and a neutral chemical flush in between to generate calcium hydroxide, Ca(OH)2 [4, 5], which is the key ingredient in cement manufacturing. The central channel is separated from either half cells by a cation exchange and an anion exchange membrane. The electrochemical performance of the proposed cement electrolyzer under a variety of operating conditions is investigated by considering the key variables in reactor design, including flow rate, temperature, catalyst choice and feed concentrations, to shed light into the practical challenges of running a 3-channel flow reactor. Our preliminary study indicates that membranes are possibly responsible for a significant fraction of the limited yields of Ca(OH)2. Specifically, the anion exchange membranes tested are susceptible to degradation in alkaline conditions displaying poor ionic conductance, whereas the cation exchange membranes allow significant water crossover. The findings from this study pave the way in understanding key components for cement electrolysis and other electrochemical systems employing 3-channel flow reactors. References DeAngelo, J., et al., Energy systems in scenarios at net-zero CO2 emissions. Nature Communications, 2021. 12(1): p. 6096. Ellis, L.D., et al., Toward electrochemical synthesis of cement—An electrolyzer-based process for decarbonating CaCO3 while producing useful gas streams. PNAS, 2019. 117(23): p.12584. Rissman, J., et al., Technologies and policies to decarbonize global industry: Review and assessment of mitigation drivers through 2070. Applied Energy, 2020. 266: p. 114848. Perego, A., et al., Electrochemical Flow Reactor for Cement Clinker Production. ECS Meeting s, 2021. MA2021-02(27): p. 840. Olsson, J.A., S.A. Miller, and M.G. Alexander, Near-term pathways for decarbonizing global concrete production.Nature Communications, 2023. 14(1): p. 4574.

Additive manufacturing of polymeric pressure die for rotary draw bending process

3D printing or additive manufacturing (AM) possesses enormous potential to benefit the manufacturing industry. Presently, rotary draw bending (RDB) is one of the most commonly used cold-forming industrial process for bending metal tubes. Pressure die is a fundamental forming tool in RDB processes, and it is conventionally made by various grades of comparatively expensive alloy steels. This research presents a novel design of a pressure die which can be 3D printed by using inexpensive polymeric filaments. In this research paper, the 3D-printed pressure die is named as “FFF-pressure die.” The material used to fabricate the FFF-pressure die is a thermoplastic polymer known as “ecoPLA.” The mechanical properties of ecoPLA are studied in relation to the process conditions of a RDB process. Firstly, an initial feasibility of using the FFF-pressure die in a RDB process is obtained by conducting a quick static stress analysis with actual process conditions. After initial feasibility, a complete RDB process is developed and simulated with actual process conditions and material properties. The FFF-pressure die is then practically fabricated by FFF 3D printer and experimentally tested on an industrial RDB machine. The results of practical experiments are compared with the simulation results. In order to make a comparison of the FFF-pressure die with the conventional metal pressure die, the simulation and practical process is also conducted with the conventional metal pressure die. A performance and cost comparison is made between the polymeric FFF-pressure die and the conventional metal pressure die. Von Mises stresses, contact forces, failure risk, and elastic deformations are analyzed. The advantages and limitations of using the FFF-pressure die in a RDB process are discussed in the end. This research intends to widen the avenue of using cost-effective and lightweight forming tools in metal forming industries.

Rotating Disk Electrode Studies of Benzaldehyde Oxidation on Group Ib Metals

Water electrolysis is a green means of producing hydrogen gas to be used as a fuel or reactant in key industrial processes such as ammonia production. The conventional system of oxygen evolution (OER) and hydrogen evolution (HER) suffers from slow kinetics at the anode and a high cell voltage limited by thermodynamics. One solution is to replace OER with another reaction such as an organic oxidation reaction that has a lower equilibrium potential and produces a valuable organic byproduct at the anode. Recent work has demonstrated that biomass-derived aldehydes such as furfural and 5-hydroxymethylfurfural can be selectively oxidized to their corresponding carboxylates and H2 on Cu-based anodes at low potentials (~0.1 VRHE) in alkaline media. In alkaline media, a hydroxide ion adds to the carbonyl carbon of an aldehyde molecule forming a gem diolate in solution phase. It is argued that dehydrogenation becomes more facile due to the weaker C-H bond of the gem diolate compared to the aldehyde form of the molecule. This may enable oxidation without the need for coadsorbed-OH. Additionally, aldehyde oxidation is only active on Au, Ag, and Cu electrodes at higher pHs.The aim of this work is to identify the conditions under which low potential oxidation of aldehydes and high anodic Faradaic efficiency to H2 occurs, namely, electrode material and applied potential. To avoid polymerization side reactions of furans in alkaline media, we studied benzaldehyde as a model compound which has also been shown to exhibit low potential oxidation and anodic H2. The activity (rate of aldehyde consumption) and selectivity (to anodic H2 versus H2O) were compared for the group Ib metals (Au, Ag, Cu), each of which demonstrates a relatively early onset potential (<0.4 VRHE) and generates anodic H2 at certain potentials. Rotating disk electrode studies with Koutecky-Levich analysis were conducted to separate mass transfer effects from kinetic current to compare the intrinsic activity of each electrode. Additionally, the average number electrons transferred per aldehyde molecule consumed was estimated from Koutecky-Levich analysis; the one-electron pathway combines the aldehyde hydrogens to H2 while the two-electron pathway requires an additional electron per aldehyde molecule to oxidize the hydrogen from the surface to form H2O. Rotating ring disk electrode studies were used to further validate the anodic H2 generation as a Pt ring was still able to oxidize H2 despite the presence of benzaldehyde. This was used as an additional means of estimating the selectivity to H2 as a function of potential.CVs for benzaldehyde oxidation on each metal are shown in Figure a. Peak current followed the order of Au > Ag >> Cu. Au reached a mass transfer limiting current, and Ag also showed an RPM dependence but did not reach full mass transfer limitations. Cu was the least active and did not show any mass transfer dependence. On the other hand, Cu has the earliest onset potential followed by Ag then Au. The average number of electrons transferred per aldehyde molecule according to RRDE measurements is shown in Figure b, where one electron represents H2 and two electrons represents H-oxidation to H2O. Cu had 100% selectivity to H2 over its entire potential window of activity (up to 0.45 VRHE). Au and Ag have high selectivity to H2 at low potentials but decreases to near zero H2 production in the range of 0.6-0.8 VRHE; the decrease in selectivity to H2 on Ag occurs slightly earlier than Au (~0.1 V). H2 selectivity is related to the inability of each metal to oxidize H to H2O at low potentials. Figure 1