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Quality control in Additive Manufacturing (AM) is vital for industrial applications in areas such as the automotive, medical, and aerospace sectors. Geometric inaccuracies caused by shrinkage and deformations can compromise the life and performance of additively manufactured components. Such deviations can be quantified using Digital Volume Correlation (DVC), which compares the Computer-Aided Design (CAD) model with the X-ray Computed Tomography (XCT) geometry of the components produced. However, accurate registration between the two modalities is challenging due to the absence of a ground truth or reference deformation field. In addition, the extremely large data size of high-resolution XCT volumes makes computation difficult. In this work, we present a deep learning-based approach for estimating voxel-wise deformations between CAD and XCT volumes. Our method uses a dynamic patch-based processing strategy to handle high-resolution volumes. In addition to the Dice score, we introduce a Binary Difference Map (BDM) that quantifies voxel-wise mismatches between binarized CAD and XCT volumes to evaluate the accuracy of the registration. Our approach shows a 9.2% improvement in the Dice score and a 9.9% improvement in the voxel match rate compared to classic DVC methods, while reducing the interaction time from days to minutes. This work sets the foundation for deep learning-based DVC methods to generate compensation meshes that can then be used in closed-loop correlations during the AM production process. Such a system would be of great interest to industry, as it would make the manufacturing process more reliable and efficient, saving time and material.
Kinetics information on the progress of the mechanochemical reactions is key to their understanding and subsequent scale-up. For crystalline materials, the most robust and tested method for obtaining kinetic data is the Quantitative Phase Analysis (QPA) via Rietveld refinement. In this work, we tested the feasibility of the Multivariate Curve Resolution-Alternating Least Squares (MCR-ALS) method on powder X-ray diffraction (PXRD) data of mechanochemical processes by studying the system theophylline (TP) and malonic acid (MA) in a 1 : 1 stoichiometric ratio at different milling conditions. We have highlighted the strengths and weaknesses of the MCR-ALS method, and we demonstrated why it may be an alternative route to obtain quantitative information on mechanochemical kinetics.
We present the racemization of an active pharmaceutical ingredient Levetiracetam using a novel approach. We demonstrate the design of a 100% solvent-free process that proceeds by high energy milling inside a regular mixer mill. The kinetics of the racemization process is drastically improved compared to the solution-based approach and illustrates the tremendous potential of mechanochemistry. In this study, we highlight the importance of mixing efficiency regarding data reproducibility, and we show, in particular, that water contamination has a negative impact on the reaction rate. Moreover, in situ X-ray diffraction gives us first insights into the mechanisms involved in the solid state during the mechanochemical racemization process.
Mechanochemical synthesis of pharmaceutical compounds has gained significant attention due to its potential to overcome traditional synthetic challenges while offering the possibility of improving the physicochemical properties of drugs. This study delves into the mechanochemical synthesis of silver sulfadiazine (AgSD) coordination compounds, obtained under different mechanochemical stress and processing conditions. The aim of this work was to investigate the influence of mechanochemical conditions on the selectivity in the preparation of AgSD coordination compounds. Through a series of experiments, we demonstrate the successful synthesis of two different AgSD coordination networks, using high-energy ball milling. By strategically manipulating the starting materials and milling parameters — including milling time, milling frequency, type of mechanical stress (as determined by different milling devices), and the presence of co-milling agents — we were able to control the product outcome. As a result, we achieved two different forms of silver-sulfadiazine metal frameworks, one of which was not previously disclosed. The crystal structure of the new form, obtained from high resolution PXRD synchrotron data, was compared with the previously known structure of a silver sulfadiazine compound. The in-depth antimicrobial activity systematic study of these AgSD forms on the generic systems showed increased antibacterial activity when compared to sulfadiazine. This research sheds light on the mechanochemical synthesis of silver sulfadiazine complexes. The obtained knowledge may guide the development of novel synthetic strategies for other drug molecules, leading to improved drug performance, stability, and therapeutic outcomes.
Arguably mechanochemistry is one of the oldest, and at the same time one of the newest, areas of chemistry. One of the oldest, because it has been theorized that early hominids were able to develop rapidly due to their ability to heat and mechanically process their food to make it easier to digest. Old, because the ancient Greeks knew that mechanically activating stones (consisting of cinnabar) with vinegar in a copper pestle and mortar caused them to convert into mercury. We might be forgiven for thinking that this could have led our alchemical forbears to wonder if there were other stones that might also be persuaded to convert into gold. Early chemical luminaries such as Michael Faraday and Wilhelm Röntgen dabbled in mechanochemistry. However, in the late 19th and early 20th centuries, the field developed separately in many areas. Perhaps most significantly, synthetic chemists realized that reactions could be carried out with surprisingly high yields and selectivities by shaking them in a ball mill, and that they could even make novel compounds that their colleagues just using heat were not able to. Physicists were mechanically rearranging atoms into new configurations (i.e., doing chemistry) using exotic means such as atomic force microscope tips and optical tweezers. Scientists and engineers studying the formation of lubricious and anti-wear films realized that they were mechanochemically formed. Biochemists found molecular motor proteins, such as myosin and kinesin, that were able to convert chemical energy into motion or, vice versa, mechanical energy into the biochemical energy storage molecule adenosine triphosphate. In 2023, this confluence of advances convinced the Royal Society of Chemistry that the time was ripe to establish a new journal dedicated to the field of mechanochemistry. It is rare that a journal has the privilege of helping to create a new sub-branch of chemistry but, having done so, it has the obligation of helping to define the field and, optimistically, suggesting how the field might develop. This editorial has two major goals. The first is to bring to the attention of the scientific community the challenges faced by the field to set people thinking about how they might contribute and, second, to form the basis for more thorough discussions in the future. The sub-topics below outline current thoughts brought forward from members of our editorial board. We lay out areas and topics here with the intent of establishing a new editorial series, which we invite the community to contribute to, and outline topics and hopefully lay out challenges, which will enable us as a community to come together to move mechanochemistry forward.
It is classically considered that the creep mechanisms for type M (e.g., pure Al) and type A alloys (e.g., Al–Mg alloys) are different. In previous studies, it is predicated that fractal dislocation structures build up during creep can unify the creep behavior of pure Al and Al–Mg alloys. So far, good agreement between model and experimental data for pure Al is obtained. In this work, an Al‐3.85%Mg material crept at different strain levels (6%, 12%, 24%, and 35%) is analyzed by means of electron channelling contrast imaging. The formation of subgrains is observed at very large deformations (35%). Further examinations at 35% deformation, using electron backscatter diffraction, indicate that the subgrains and cavities tend to localize at regions where high intergranular stress mismatch is expected to occur. Laboratory X‐ray computed tomography is used to analyze the evolution of cavities between 12% and 24% stages, indicating that the fractal dimension of the cavities smaller than 100 μm varies with creep strain, as a reflection of the evolution of dislocation distribution. It is explained how the present data feed the model of the creep behavior of Al‐3.85%Mg.
The use of supplementary cementitious materials (SCM) is an important part of the roadmap for reducing CO2 emissions and extending the service life of reinforced concrete structures. To accelerate the adoption of SCMs, the RILEM Technical Committee 298-EBD evaluates scaled-down cement paste test methods to assess the effect of SCM on resistance to chloride and sulfate ingress and reactivity, which are critical to concrete durability. This review focuses on methods for measuring chloride diffusivity and is divided into four sections: diffusivity models and parameters, diffusion test methods (including NMR and chloride measurements), migration test methods and implications for future research. Key insights highlight the complexities of multi-species ionic and molecular diffusion/migration, including various binding interactions, and compares the different measurement methodologies. The review also addresses the test scale and aggregate effects, noting the pros and cons of testing at the paste, mortar, and concrete scales. The review underscores the need for further investigation into testing protocols and the influence of SCM on chloride diffusion, emphasizing that comprehensive testing across different scales provides complementary information for assessing durability performance.
The increasing integration of automation and artificial intelligence (AI) in non-destructive testing (NDT) is not only changing the inspection processes themselves, but also the way decisions are made. While technical systems can reduce error-proneness and support data processing, the ultimate responsibility remains with the human. This paper examines the role of intuition in decision-making and analyses typical errors of judgment using prospect theory and insights from cognitive psychology. It also shows how well-informed decisions can be supported in AI-supported NDT processes - through training, explainable systems, user-centred design, suitable metrics, and a targeted distribution of tasks between people and technology. Rather than replacing human intuition, AI systems should be designed to complement it. To engage effectively with such systems, inspectors require not only technical expertise, but also competencies in risk assessment, probabilistic reasoning, and critical reflection on both their own judgments and the outputs provided by AI.
The control of heat in energy materials is one of the greatest current engineering challenges. Accurate estimations of heat capacity are key in creating and using materials safely and efficiently. Current models for heat capacity are often limited due to crude estimations of the phonon density of states, which is a key component of the thermodynamic definition of heat capacity. Utilization of a more detailed phonon density of states, which can easily be obtained from machine-learned algorithms, combined with dilation and electronic contributions, yields heat capacity estimations that are 29% better than the widely utilized Debye model and are comparable to state-of-the-art quantum mechanical calculations. The framework and necessary tools for heat capacity estimations demonstrated herein can be built into more detailed models and analyses, such as high-throughput characterization, transport models, or other thermodynamic assessments. Consequently, the so-called vibrational + dilation + electronic (VDE) model of heat capacity developed in this work better enables the informed design of next-generation devices.
Infrared thermography is a widely recognized non-destructive testing (NDT) method used in material research and defect detection across various industrial applications. Moreover, thermography plays a crucial role in preserving cultural heritage, including historical paintings and buildings. This study focuses on the application of thermography in inspecting the historic Bücker Bü 181 aircraft, which was used in Germany during World War II. Over time, the original appearance of aircraft has often been altered as part of preservation efforts, either before or during their time in museums, leading to deviations from their historically original state. Additionally, the operational history of such objects is frequently undocumented or entirely lost, making it difficult to understand the presence of artifacts and historically significant data. These factors present major challenges in cultural heritage preservation, and destructive methods cannot be used to investigate such invaluable objects.
Therefore, thermography is implemented as a non-destructive and contactless examination method. Active flash thermography combined with phase analysis is a powerful tool for evaluating multilayer systems. In this study, multiple layers of old paint on the object posed a challenge in assessing defect conditions and retrieving other critical information beneath the surface coatings. Nevertheless, pulse thermography not only demonstrated its capability to identify defects and markings in multilayered coatings but also provided insights into the internal structure and subsections of the investigated aircraft.