Material Processing Conditions Research Articles

A Novel Computational Approach Coupled with Machine Learning to Predict the Extent of Agglomeration in Particulate Processes.

Solid particle agglomeration is a prevalent phenomenon in various processes across the chemical, food, and pharmaceutical industries. In pharmaceutical manufacturing, agglomeration is both desired in unit operations like wet granulation and undesired in unit operations such as agitated filter drying of highly potent active pharmaceutical ingredients (API). Agglomeration needs to be controlled for optimal physical properties of the API powder. Even after decades of work in the field, there is still very limited understanding of how to quantify, predict, and control the extent of agglomeration, owing to the complex interaction between the solvent and the solid particles and stochasticity imparted by mixing. Furthermore, a large size of industrial scale particulate process systems makes it computationally intractable. To overcome these challenges, we present a novel theory and computational methodology to predict the agglomeration extent by coupling the experimental measurements of agglomeration risk zone or "sticky zone" with discrete element method. The proposed model shows good agreement with experiments. Further, a machine learning model was built to predict agglomeration extent as a function of input variables, such as material properties and processing conditions, in order to build a digital twin of the unit operation. While the focus of the present study is the agglomeration of particles during industrial drying processes, the proposed methodology can be readily applied to numerous other particulate processes where agglomeration is either desired or undesired.

Synergistic Effects of Surface Coating and Bulk Doping in Ni-Rich Lithium Nickel Cobalt Manganese Oxide Cathode Materials for High-Energy Lithium Ion Batteries.

Ni‐rich layered oxide cathodes are promising candidates to satisfy the increasing energy demand of lithium‐ion batteries for automotive applications. Thermal and cycling stability issues originating from increasing Ni contents are addressed by mitigation strategies such as elemental bulk substitution (“doping”) and surface coating. Although both approaches separately benefit the cycling stability, there are only few reports investigating the combination of two of such approaches. Herein, the combination of Zr as common dopant in commercial materials with effective Li2WO4 and WO3 coatings was investigated with special focus on the impact of different material processing conditions on structural parameters and electrochemical performance in nickel‐cobalt‐manganese (NCM) || graphite cells. Results indicated that the Zr4+ dopant diffusing to the surface during annealing improved the electrochemical performance compared to samples without additional coatings. This work emphasizes the importance to not only investigate the effect of individual dopants or coatings but also the influences between both.

Squeeze flow in multilayer polymeric films: Effect of material characteristics and process conditions

AbstractIn this work, effects of sealing temperature, time, pressure, as well as sealant thickness and viscosity on squeeze out flow (SOF) in heat sealing were examined. A new image analysis approach is presented to quantify SOF in heat sealing. It was found that increasing temperature or pressure could improve SOF but only in thick 130 μm sealants and reducing the sealant thickness to 50 μm suppressed SOF. Reducing viscosity in 50 μm sealant films was also found to improve SOF only at high‐sealing pressure and long sealing times. Three approaches were used to model SOF: analytical one‐dimensional model, numerical one‐dimensional model using finite difference method (FDM), (iii) Numerical two‐dimensional model using finite element analysis. Heat transfer was modeled, and it was shown that heat transfer induces a delay in SOF. When the FDM and the heat transfer models were combined, a good agreement between experimental and model prediction could be obtained. In addition, modeling results showed that SOF occurred in shear rates within the transition region between the Newtonian and Power‐law regions. This indicates the importance of considering the Carreau‐Yasuda fluid behavior in modeling of SOF.

Characterizing the Effects of Synergistic Thermal and Photo-Cross-Linking during Biofabrication on the Structural and Functional Properties of Gelatin Methacryloyl (GelMA) Hydrogels.

Gelatin methacryloyl (GelMA) hydrogels have emerged as promising and versatile biomaterial matrices with applications spanning drug delivery, disease modeling, and tissue engineering and regenerative medicine. GelMA exhibits reversible thermal cross-linking at temperatures below 37 °C due to the entanglement of constitutive polymeric chains, and subsequent ultraviolet (UV) photo-cross-linking can covalently bind neighboring chains to create irreversibly cross-linked hydrogels. However, how these cross-linking modalities interact and can be modulated during biofabrication to control the structural and functional characteristics of this versatile biomaterial is not well explored yet. Accordingly, this work characterizes the effects of synergistic thermal and photo-cross-linking as a function of GelMA solution temperature and UV photo-cross-linking duration during biofabrication on the hydrogels' stiffness, microstructure, proteolytic degradation, and responses of NIH 3T3 and human adipose-derived stem cells (hASC). Smaller pore size, lower degradation rate, and increased stiffness are reported in hydrogels processed at lower temperature or prolonged UV exposure. In hydrogels with low stiffness, the cells were found to shear the matrix and cluster into microspheroids, while poor cell attachment was noted in high stiffness hydrogels. In hydrogels with moderate stiffness, ones processed at lower temperature demonstrated better shape fidelity and cell proliferation over time. Analysis of gene expression of hASC encapsulated within the hydrogels showed that, while the GelMA matrix assisted in maintenance of stem cell phenotype (CD44), a higher matrix stiffness resulted in higher pro-inflammatory marker (ICAM1) and markers for cell-matrix interaction (ITGA1 and ITGA10). Analysis of constructs with ultrasonically patterned hASC showed that hydrogels processed at higher temperature possessed lower structural fidelity but resulted in more cell elongation and greater anisotropy over time. These findings demonstrate the significant impact of GelMA material formulation and processing conditions on the structural and functional properties of the hydrogels. The understanding of these material-process-structure-function interactions is critical toward optimizing the functional properties of GelMA hydrogels for different targeted applications.

3D/4D Printing of Polymers: Fused Deposition Modelling (FDM), Selective Laser Sintering (SLS), and Stereolithography (SLA).

Additive manufacturing (AM) or 3D printing is a digital manufacturing process and offers virtually limitless opportunities to develop structures/objects by tailoring material composition, processing conditions, and geometry technically at every point in an object. In this review, we present three different early adopted, however, widely used, polymer-based 3D printing processes; fused deposition modelling (FDM), selective laser sintering (SLS), and stereolithography (SLA) to create polymeric parts. The main aim of this review is to offer a comparative overview by correlating polymer material-process-properties for three different 3D printing techniques. Moreover, the advanced material-process requirements towards 4D printing via these print methods taking an example of magneto-active polymers is covered. Overall, this review highlights different aspects of these printing methods and serves as a guide to select a suitable print material and 3D print technique for the targeted polymeric material-based applications and also discusses the implementation practices towards 4D printing of polymer-based systems with a current state-of-the-art approach.

Monolithic carbon aerogels from bioresources and their application for CO2 adsorption

Monolithic binder-free CO 2 adsorbents with high adsorption capacity, selectivity, adsorption-desorption kinetics, and regenerability are highly desired to both reduce the environmental impact of anthropogenic CO 2 emissions and purify valuable gases from CO 2 . Herein, we report a strategy to prepare monolithic carbonaceous CO 2 adsorbents from low-cost and underutilized bioresources, which enabled the formation of a delicate anisotropic, hierarchical porous structure. With optimized material composition and processing conditions, the biobased carbon adsorbent demonstrated a CO 2 adsorption capacity of 4.49 mmol g -1 at 298 K and 100 kPa, relatively weak adsorbent-adsorbate affinity, good CO 2 /N 2 selectivity, and advantageous hydrophobicity against water vapor. Moreover, the unique anisotropic porous structure provided high stiffness and good flexibility to the adsorbent in the axial and radial directions, respectively. We confirmed that this type of carbon adsorbent could be packed in a column for dynamic CO 2 capture independent of any binders, indicating its promising future for further development toward widespread utilization. • Biocarbon aerogels with anisotropic, hierarchical porous structure were prepared. • Carbon aerogels reached CO 2 adsorption capacity of 4.49 mmol g −1 (298 K, 100 kPa). • Carbon aerogels showed good CO 2 /N 2 selectivity and low water vapor adsorption. • Carbon aerogels exhibited strong and unique anisotropic mechanical properties. • Carbon aerogels were packed in a column without binders for large-scale CO 2 capture.

Crust breakage in production of fine particles using pulse combustion drying: Experimental and numerical investigations

The mechanism that leads to fine particle production during pulse combustion drying is still unclear. Although droplet breakup could happen, it cannot result in particles below micron-size. Here production of fine particles during pulse combustion drying is investigated experimentally and numerically. Experiments with three different materials, namely SiO2, TiO2, and maltodextrin, have been conducted to see how the material types and process conditions influence the physical properties of the products. The results show that fine particles can be obtained with and without pulsation, and no significant differences are observed with the variation of pulsation frequency. The volume fraction of the fine particles is, however, very low. Based on these results, a model of particle production through repetitive crust breakage and drying cycles is proposed. The obtained results are in agreement with the experimental data, and the proposed mechanism could explain the production of fine particles with and without gas pulsation.

Understanding the Microstructure Formation of Polymer Films by Spontaneous Solution Spreading Coating with a High-Throughput Engineering Platform.

An important step of the great achievement of organic solar cells in power conversion efficiency is the development of low‐band gap polymer donors, PBDB−T derivatives, which present interesting aggregation effects dominating the device performance. The aggregation of polymers can be manipulated by a series of variables from a materials design and processing conditions perspective; however, optimization of film quality is a time‐ and energy‐consuming work. Here, we introduce a robot‐based high‐throughput platform (HTP) that is offering automated film preparation and optical spectroscopy thin‐film characterization in combination with an analysis algorithm. PM6 films are prepared by the so‐called spontaneous film spreading (SFS) process, where a polymer solution is coated on a water surface. Automated acquisition of UV/Vis and photoluminescence (PL) spectra and automated extraction of morphological features is coupled to Gaussian Process Regression to exploit available experimental evidence for morphology optimization but also for hypothesis formulation and testing with respect to the underlying physical principles. The integrated spectral modeling workflow yields quantitative microstructure information by distinguishing amorphous from ordered phases and assesses the extension of amorphous versus the ordered domains. This research provides an easy to use methodology to analyze the exciton coherence length in conjugated semiconductors and will allow to optimize exciton splitting in thin film organic semiconductor layers as a function of processing.

Impact of 2D-3D Heterointerface on Remote Epitaxial Interaction through Graphene.

Remote epitaxy has drawn attention as it offers epitaxy of functional materials that can be released from the substrates with atomic precision, thus enabling production and heterointegration of flexible, transferrable, and stackable freestanding single-crystalline membranes. In addition, the remote interaction of atoms and adatoms through two-dimensional (2D) materials in remote epitaxy allows investigation and utilization of electrical/chemical/physical coupling of bulk (3D) materials via 2D materials (3D-2D-3D coupling). Here, we unveil the respective roles and impacts of the substrate material, graphene, substrate-graphene interface, and epitaxial material for electrostatic coupling of these materials, which governs cohesive ordering and can lead to single-crystal epitaxy in the overlying film. We show that simply coating a graphene layer on wafers does not guarantee successful implementation of remote epitaxy, since atomically precise control of the graphene-coated interface is required, and provides key considerations for maximizing the remote electrostatic interaction between the substrate and adatoms. This was enabled by exploring various material systems and processing conditions, and we demonstrate that the rules of remote epitaxy vary significantly depending on the ionicity of material systems as well as the graphene-substrate interface and the epitaxy environment. The general rule of thumb discovered here enables expanding 3D material libraries that can be stacked in freestanding form.

Phase-Inversion Polymer Composite Separators for Printable Lithium-Ion Batteries

Separators isolate the anode and cathode electrodes in Lithium-ion (Li-ion) batteries and play a critical role in facilitating ion transport while enabling safe battery operation. Commercial polyolefin separators made of polypropylene (PP) and polyethylene (PE) are used widely in traditional liquid electrolyte Li-ion batteries. However, these materials have limited thermal stability and are not compatible with additive manufacturing approaches for fabrication of new battery electrode architectures [1]. Researchers have investigated phase-inversion composite separators as a means to solve these problems, but there is limited data available on the relative effects of material composition and processing conditions on final separator morphology and performance. In our work, we analyze the effects of solvents, inorganic additives and phase inversion processing conditions on the separator’s ionic conductivity, rate capability, and mechanical integrity. Silica (SiO2), alumina (Al2O3), and glass particles are used as inorganic additives in varying combinations and ratios in a polyvinylidene fluoride (PVDF) polymer matrix. The PVDF concentration in our solvent solution is varied to understand the impact of solution viscosity on the phase inversion process and resultant mechanical performance of our separators. Through this study we begin to elucidate relationships between processing conditions, material composition and electrochemical and mechanical performance to facilitate further development of alternative Li-ion battery separators that are compatible with additive manufacturing.[1] Cobb, C. L.; Ho, C. C. Electrochem. Soc. Interface 2016, 25 (1), 75–78. Acknowledgement This work was funded in part by a Defense Advanced Research Projects Agency (DARPA) Young Faculty Award under grant number D19AP00038. The views, opinions, and/or findings expressed are those of the authors and should not be interpreted as representing the official views or policies of the Department of Defense or the U.S. Government.

Optical Index Matching, Flexible Electrospun Substrates for Seamless Organic Photocapacitive Sensors

Recent advances in optoelectronics are often based on thin‐film organic semiconductors. Interesting organic semiconductors are given by squaraines, small molecules that show excitonic coupling with visible light and are thus suitable for applications in solar cells and light sensors. While such squaraine thin films have already been proven to be suitable for stimulation of neuronal model cells, the integration into, e.g., the human eye to support blind people necessitates forming thin layers on seamless substrates. Herein, squaraine films are spin‐coated on electrospun nanofiber mats and nanomembranes, prepared from polyacrylonitrile, and made conductive by spin coating with poly(3,4‐ethylenedioxythiophene) polystyrene sulfonate (PEDOT:PSS). The fibrous non‐woven texture of the nanofiber mats and membranes alters the thin film formation of the squaraine compound considerably compared with preparation on planar, nonsoaking substrates such as glass and polyethylene terephthalate (PET) foil demanding further engineering regarding material's choice and processing conditions.

A processing route to spherical polymer particles via controlled droplet retraction

In this work we describe the production of spherical polymer particles from thermoplastic polymers. A simple method is developed to transform rough powders into spherical particles. The polymer particles are molten in heated oil whereafter interfacial tension driven reduction of curvature and shape retraction induce a transformation to the equilibrium spherical shape. The sphericity of the particles is validated by determining the aspect ratio, roundness, as well as circularity. Furthermore, batches of narrow particle size distribution are attained by sieving on different mesh sizes. The effects of the material characteristics and processing conditions are investigated and rationalized based on the governing physical processes and their scaling relations. As a result, the processing conditions can a priori be tailored depending on the material characteristics. The versatility of the method is confirmed by generating spherical particles from various thermoplastic polymers including PS, LDPE, PP, PA12 and PA12 filled with carbon nanotubes.

Finite Element-Based Simulation of Metal Fused Filament Fabrication Process: Distortion Prediction and Experimental Verification

Building end-use functional metal parts by metal fused filament fabrication (MF3) is an emerging topic in additive manufacturing. MF3 involves extrusion of polymer filaments that are highly filled with metal powder to print three-dimensional parts, followed by debinding and sintering to eliminate the polymer binder and get a fully dense metal part, respectively. Material properties, part design and processing conditions have a significant influence on the quality of MF3 printed parts. Part distortion and dimensional variations are significant quality challenges that hinder the acceptance of printed parts in potential functional applications. Trial-and-error experiments to find the best conditions are commonly used for defect avoidance, though they are time-consuming and expensive. Hence, computational simulation and design solutions are required for MF3 to enable a virtual analysis of the process outcome and reduce dependency on experimental methods. This paper investigates the applicability of a thermo-mechanical model for finite element simulation of the MF3 printing process. The quantitative influence of material properties on MF3 printed part quality was estimated using a simulation platform. The simulation results of two materials, a Ti-6Al-4V filled polymer and an unfilled ABS copolymer, were compared to experiments. It was determined that the unfilled polymer showed greater shrinkage and warpage than the Ti-6Al-4V filled polymer in simulations and experiments. Further, the trend in the distribution of warpage was consistent between experiments and simulation results for both materials. Finally, warpage compensation algorithms showed improvement in dimensional control for both materials in simulations and were consistent with experimental results.

Effects of TiN Top Electrode Texturing on Ferroelectricity in Hf1-xZrxO2.

Ferroelectric memories based on hafnium oxide are an attractive alternative to conventional memory technologies due to their scalability and energy efficiency. However, there are still many open questions regarding the optimal material stack and processing conditions for reliable device performance. Here, we report on the impact of the sputtering process conditions of the commonly used TiN top electrode on the ferroelectric properties of Hf1–xZrxO2. By manipulating the deposition pressure and chemistry, we control the preferential orientation of the TiN grains between (111) and (002). We observe that (111) textured TiN is superior to (002) texturing for achieving high remanent polarization (Pr). Furthermore, we find that additional nitrogen supply during TiN deposition leads to >5× greater endurance, possibly by limiting the scavenging of oxygen from the Hf1–xZrxO2 film. These results help explain the large Pr variation reported in the literature for Hf1–xZrxO2/TiN and highlights the necessity of tuning the top electrode of the ferroelectric stack for successful device implementation.

Determining the glycaemic responses of foods: conventional and emerging approaches

A low-glycaemic diet is crucial for those with diabetes and cardiovascular diseases. Information on the glycaemic index (GI) of different ingredients can help in designing novel food products for such target groups. This is because of the intricate dependency of material source, composition, food structure and processing conditions, among other factors, on the glycaemic responses. Different approaches have been used to predict the GI of foods, and certain discrepancies exist because of factors such as inter-individual variation among human subjects. Besides other aspects, it is important to understand the mechanism of food digestion because an approach to predict GI must essentially mimic the complex processes in the human gastrointestinal tract. The focus of this work is to review the advances in various approaches for predicting the glycaemic responses to foods. This has been carried out by detailing conventional approaches, their merits and limitations, and the need to focus on emerging approaches. Given that no single approach can be generalised to all applications, the review emphasises the scope of deriving insights for improvements in methodologies. Reviewing the conventional and emerging approaches for the determination of GI in foods, this detailed work is intended to serve as a state-of-the-art resource for nutritionists who work on developing low-GI foods.

Microstructure and pore structure of polymer-cement composite joint sealants

Utilizing methods such as scanning electron microscopy observation and mercury intrusion porosimetry, this paper investigates the basic microstructure and pore structure properties of polymer-cement composite joint sealants for pavements, and analyzes the effects and rules of various material types, ratio parameters and processing conditions. Further, the fractal characteristics and variation rules of pore size distribution are investigated for the joint sealants by introducing the fractal theory. The results show that changes in material type, ratio parameter and processing condition produce insignificant effects on the basic microstructure properties and configuration of joint sealants, with effects reflected primarily in the change of sealant pore structure. Measures like increasing the powder-liquid ratio and cement ratio, blending with sulphoaluminate cement or mica powder, adding latex powder or coupling agent, cold drawing and hot pressing, as well as ultraviolet irradiation treatment are all capable of reducing the total pore volume of joint sealants and refining their pore structure. In contrast, opposite effects are yielded when low-grade cement is used, styrene-acrylic emulsion is blended, or plasticizer is added. Additionally, after blending with talc powder or adding carbon fiber additive, the total pore volume of joint sealants remains basically unchanged or reduced, despite the coarsened pore structure. The total pore volume of joint sealants increases after wet–dry cycling treatment, while no obvious change in the pore size distribution is observed. Pore size distribution of the studied joint sealants presents distinct fractal characteristics, and the corresponding fractal dimension of pore surface area ranges between 2.6 and 2.8.

Layer-by-layer fabrication of organic photovoltaic devices: material selection and processing conditions

Layer-by-layer (LbL) processing, otherwise known as sequential deposition, is emerging as the most promising strategy for fabrication of active layers in organic photovoltaic (OPV) devices on both laboratory and industrial scales.

Experimental and stochastic analysis of lyophilisation

The development of the freeze-drying processes through the use of a combination of targeted experiments and the application of multidimensional computational models is applied increasingly in pharmaceutical practice, especially for scale-up purposes. This study deals with the analysis of uncertainties in the data on material properties and model parameters, and their influence on the results delivered by advanced computational models of lyophilisation. As a means of uncertainty analysis, the Stochastic Collocation Method is applied, allowing the use of existing reliable deterministic models as black boxes in the stochastic computations. As a deterministic model, the lyophilisation model is used, based on the axisymmetric approximation of a vial, and the Boundary Element Method as a solver. Five parameters, covering material properties, process conditions and model constants, are selected for the sensitivity analysis simulation of the lyophilisation of an aqueous mannitol solution. The results show that during the initial stage of the primary drying heat transfer from the shelf is crucial, and that the uncertainties in the contact surface area and material properties of the vial play a more important role than the thermal properties of the drying material. When the temperature of the material reaches its distinct primary drying stage level the mass transfer through the porous cake becomes the most important, with great sensitivity to the Knudsen diffusivity in the porous cake. We observed uncertainties in the results for the primary drying time in the order of ±6%, and uncertainties in the results for temperatures of ±0.6°C in the frozen material and ±3°C in the porous cake. The uncertainty analysis proved to be a great help in determining the critical parameters in the heat and mass transfer during the important primary drying step, which led to a better definition of the numerical model for use in the context of design space determination.

Additive manufacturing of biodegradable magnesium implants and scaffolds: Review of the recent advances and research trends

Synthetic grafting needs improvements to eliminate secondary surgeries for the removal of implants after healing of the defected tissues. Tissue scaffolds are engineered to serve as temporary templates, which support the affected tissue and gradually degrade through the healing period. Beside mechanical function to withstand the anatomic loading conditions, scaffolds should also provide a decent biological function for the diffusion of nutrients and oxygen to the cells, and excretion of the wastes from the cells to promote the new tissue growth and vascularization. Moreover, the degradation byproducts of the scaffolds should be safe to the human body. Development of such multifunctional scaffolds requires selection of the right material, design, and manufacturing method. Mg has been recognized as the prominent biodegradable metal with regards to its mechanical properties matching to that of human bone, degradability in the body fluid, and its ability to stimulate new tissue growth. Scaffolds with intricate porous structures can be designed according to the patient-specific anatomic data using computer aided designs. Additive manufacturing (AM) is the right method to materialize these models rapidly with reasonably acceptable range of dimensional accuracy. Thus, the recent research trend is to develop ideal scaffolds using biodegradable Mg through AM methods. This review compiles and discusses the available literature on the AM of biodegradable Mg parts from the viewpoints of material compositions, process conditions, formation quality, dimensional accuracy, microstructure, biodegradation, and mechanical properties. The current achievements are summarized together, and future research directions are identified to promote clinical applications of biodegradable Mg through the advancement of AM.

Image-driven discriminative and generative machine learning algorithms for establishing microstructure–processing relationships

We investigate the methods of microstructure representation for the purpose of predicting processing condition from microstructure image data. A binary alloy (uranium–molybdenum) that is currently under development as a nuclear fuel was studied for the purpose of developing an improved machine learning approach to image recognition, characterization, and building predictive capabilities linking microstructure to processing conditions. Here, we test different microstructure representations and evaluate model performance based on the F1 score. A F1 score of 95.1% was achieved for distinguishing between micrographs corresponding to ten different thermo-mechanical material processing conditions. We find that our newly developed microstructure representation describes image data well, and the traditional approach of utilizing area fractions of different phases is insufficient for distinguishing between multiple classes using a relatively small, imbalanced original dataset of 272 images. To explore the applicability of generative methods for supplementing such limited datasets, generative adversarial networks were trained to generate artificial microstructure images. Two different generative networks were trained and tested to assess performance. Challenges and best practices associated with applying machine learning to limited microstructure image datasets are also discussed. Our work has implications for quantitative microstructure analysis and development of microstructure–processing relationships in limited datasets typical of metallurgical process design studies.