Antibody-based therapeutics represent an important class of biopharmaceuticals [1]. Crystallization of antibodies is essential for structural characterization and holds potential for applications in downstream processing and drug formulation [2]. However, crystallization of antibodies remains challenging due to the large size, conformational flexibility, and complex intermolecular interactions of antibodies [3]. Predicting mutations that influence crystallizability could facilitate rational design of crystallization strategies, yet the limited availability of structural data has restricted the development of robust predictive models. In this study, we combine computational modelling with machine learning to identify descriptors associated with crystallization in nanobodies as a proof-of-concept system, given the scarcity of crystallized full-length monoclonal antibodies. Monomeric nanobody structures were curated and analyzed to identify crystal interface residues, which were classified as crystal-site or non-crystal-site residues. Each residue was represented using a multidimensional set of physicochemical and structural descriptors capturing both intrinsic residue properties and features of the surrounding structural environment. Several machine learning algorithms were evaluated for residue classification, among which XGBoost demonstrated the best predictive performance. Here, we present our preliminary analysis revealing structural characteristics associated with crystallization propensity, providing insights into residue-level determinants of crystal formation. This is one of the first fundamental steps towards a framework aimed at enhancing crystallization of antibodies through mutation-driven crystal contact engineering.

References:

1. Walsh, G., Walsh, E. Biopharmaceutical benchmarks 2022. Nat Biotechnol 40, 1722–1760 (2022). https://doi.org/10.1038/s41587-022-01582-x

2.Zang Y, Kammerer B, Eisenkolb M, Lohr K, Kiefer H (2011) Towards Protein Crystallization as a Process Step in Downstream Processing of Therapeutic Antibodies: Screening and Optimization at Microbatch Scale. PLoS ONE 6(9): e25282. https://doi.org/10.1371/journal.pone.0025282

3.Chattaraj et al (2025c). Investigating structural biophysical features for antigen-binding fragment crystallization via machine learning. Molecular Systems Design & Engineering, 10(5), 377–393. https://doi.org/10.1039/d4me00187g

Acknowledgements

We acknowledge the PROCRYSTAL consortium for supporting this research, funded under Grant Agreement ID: 101169471.

The microstructure evolution plays an important role in the process of static recrystallization occurring during heat treatment following plastic deformation. The process is affected by the deformation energy stored in the material and the temperature of the heat treatment of finite duration. Key parameters are the density and spatial distribution of nucleation sites in the initial polycrystalline microstructure. Similarly, key modelling assumptions are the distribution of the moving speed of the recrystallization front, and whether nucleation is site-saturated or continuous.
The simulations are done on three-dimensional rectangular subblocks using the principles of the Monte Carlo Potts model. The effect of the previous deformation was defined by the elongated shape of grains in the initial microstructure. The nucleation happens with a defined probability for internal cells, surfaces and edges on the grain boundaries, and triple junctions. Outer surfaces are defined as either periodic or rigid boundary conditions. Continuous nucleation is defined as a constant generation possibility on each unrecrystallized cell over time.
The JMAK (Johnson–Mehl–Avrami–Kolmogorov) theory describes recrystallization kinetics using a shape exponent and a scale parameter. An important result is finding the relation between the coefficients in the general JMAK model, and the parameters of the current simulation with various initial assumptions. Simulations with site-saturated nucleation precisely reproduce the JMAK model with three-dimensional growth, while continuous nucleation causes higher exponents, similarly to the theoretical values.
The developed model can simulate the static recrystallization of the crystalline materials, with a good correlation with measured recrystallization fraction from measured hardnesses. A further aim is taking into consideration the effect of the grain boundary misorientation and the orientations of the grains on the nucleation process.
Supported by the EKÖP-25 University Excellence Scholarship Program of the Ministry for Culture and Innovation from the source of the National Research, Development and Innovation Fund.

High and medium-entropy alloys are promising candidates for solid-state hydrogen storage; however, their practical implementation remains constrained by demanding activation procedures, limited understanding of long-term cyclic stability, and the necessity for precise microstructural optimization to ensure favorable hydrogen sorption kinetics and reversible storage performance. This study evaluates the effect of microstructure on the hydrogen storage performance of a quaternary, medium-entropy TiVCrMn alloy under moderate pressure and temperature conditions.

Calphad simulations predict a dual-phase BCC + C14 Laves phase microstructure for TiVCrMn below 800 °C. The alloy was produced by arc-melting and characterized by XRD, SEM-EDS, and STEM-EDS, confirming the predicted phase constitution. Disks cut from the as-cast ingot were processed by high-pressure torsion, HPT (5 GPa, 1 rpm, 10 turns), leading to ultrafine microstructures with crystallite sizes of 20-50 nm.

Hydrogenation experiments demonstrated that the HPT process improved the resistance to deactivation. The HPT-processed medium-entropy alloy absorbed 1.6 wt.% hydrogen at 45 °C without requiring any prior activation treatment, exhibiting rapid kinetics (~50 min to reach saturation) and full reversibility in two stages: one at room temperature and the other at 300 °C.

The results indicate that hydrogen absorption occurs predominantly in the BCC phase and that nanostructuring via severe plastic deformation enhances hydrogen uptake and stability against deactivation, highlighting its potential to tailor medium-entropy alloys for hydrogen storage applications.

Controlling photoreactivity in organic solids has attracted significant interest from chemists and material scientists for a long time. Although the pioneering works in this field date back to the 1970s when Schmidt formulated the topochemical principle based on cinnamic acid derivatives, the advent of the subject of crystal engineering allowed scientists to tune photoreactivity in a supramolecular way using hydrogen bonding and other non-covalent interactions. The design of organic solids became easier with the formulation of the concept of supramolecular synthons. However, over the years it has transpired that small synthons, despite capturing necessary chemical and geometric details, are often insufficient to provide sufficient control in many design strategies. In this context, the idea of large synthons was coined. Large synthons, in the present context, represent modular synthons often involving composite interactions which contain sufficient chemical and geometrical information for a targeted design and provide sufficient structural insulation that makes the strategy robust and general. These modular large synthons are often considered a starting point in Long Range Synthon Aufbau Module (LSAM)-based design strategies. Despite their potential in crystal design, robust large synthons are often difficult to implement in design strategies as they are generally formed by the combined effects of weak interactions and may lack in recurrence. This talk will focus on how a robust large synthon can be helpful, especially in the context of bi-component crystal design, based on benzilic acid and stilbazole derivatives. Benzilic acid, a well-known molecule in the context of Benzil-Benzilic acid rearrangement, also contains intriguing supramolecular features that help in large synthon formation. The key focus of the talk will address: (i) crystal design with diverse photoresponses, (ii) tuning photoreactivity/photoresponses in the respective solids through a large synthon approach, and (iii) the role of modularity of such large synthons in crystal design.

The additively manufactured (AM) AlSi10Mg aluminum alloy exhibits highly anisotropic microstructures that depend on process parameters and build orientation design. The process optimization and part-building orientations influence the mechanical properties of the materials. In this investigation, we studied the influence of build orientation, such as 0°, 60°, and 90° relative to the build platform, on the crystalline deformation mechanisms of AM-processed AlSi10Mg. We used the X-ray diffraction (XRD) method to characterize the Charpy test specimens. The Charpy test is one of the most popular impact experiments that provides a measurement of the impact energy absorption behavior and toughness of the tested parts. The XRD experiment provides characteristics of the build-orientation-dependent behavior of the Charpy-tested parts. We used the Williamson–Hall method to study the XRD spectrum of the parts produced at different build orientations. The Williamson–Hall (WH) method is one of the renowned methods to investigate the stress–strain behavior of crystals. We used three WH methods, which are known as the uniform deformation model (UDM), the uniform stress deformation model (USDM), and the uniform deformation energy density model (UDEDM), for studying the crystal size, lattice stress, and strain. The results demonstrate that XRD is a powerful tool for finding the fundamental relationships between the additive manufacturing process parameters and the resulting crystal lattice integrity. It also provides crucial insights for the process optimization and mechanical performance prediction of AlSi10Mg components. We found that the 0° (horizontal) samples exhibited different deformation behavior compared to the 60° and 90° (vertical) samples, which correlates with the previously reported mechanical anisotropy of the materials.

Even if ZnO NPs have been widely investigated, they are still relevant given their versatility in various fields of application. Hence there is a necessity to advance techniques and synthesize ZnO NPs in a highly controlled manner to enhance their properties.

It is well known that the properties of nanoparticles depend mostly on the size and the homogeneity of their crystalline structure. Hydrothermal synthesis is a promising technique, as it promotes the highly controlled synthesis of nanoparticles at low temperatures and short reaction times, without the need for expensive equipment and the production of polluting effluents.

This study achieved not only the synthesis of ZnO NPs but also the crystallization of Zn_1-xFe_xO solid solution NPs with the inclusion of Fe²⁺ ions in quantities from 1 to 10% in the zinc oxide wurtzite structure. These nanoparticles were synthesized through hydrothermal reactions, carried out on Teflon-lined stainless-steel autoclaves at a temperature of 160 °C and a reaction time of only 1 hour using Zn(NO₃)₂ 6H₂O as the zinc ion precursor, FeCl₂·4H₂O as iron atoms and NaOH solutions as hydrothermal media.

The synthesized nanoparticles were analyzed using XRD, which presented highly defined peaks proving the exclusive crystallization of ZnO NPs and their gradual shift to lower angles, matching with the expansion of the lattice of the unit cell, from 45 nm to 53 nm with 10% Fe²⁺ substitution. Also, the Zn_1-xFe_xO nanoparticles exhibited a notable change in colour and hue relative to the ZnO NPs. Finally, DLS indicated a size of about 60 nm and high homogeneity of the NPs. Meanwhile, UV-Vis spectroscopy indicated the formation of Zn_1-xFe_xO nanoparticles.

Solvent selection plays a critical role in pharmaceutical crystallization, affecting product quality, manufacturability, regulatory compliance, and environmental impacts. With the growing emphasis on sustainability and life cycle assessment (LCA) in regulatory frameworks, solvent design must go beyond thermodynamic considerations to actively incorporate environmental responsibility. However, current practices still heavily rely on expert heuristics and trial-and-error experimentation, which are time-consuming and difficult to scale. Although methods such as Computer-Aided Molecular Design and other commercial tools are available, they often experience high complexity, limited usability, and inadequate integration of sustainability metrics. To address these limitations, we developed a hybrid, data-driven framework with two primary goals: (a) to identify green solvents or binary mixtures that meet solubility, sustainability, and uncertainty criteria for a given API, and (b) to provide a user-friendly, computationally efficient platform for pharmaceutical engineers. The framework includes a solubility database of over 60,000 data points across 1,183 solvent systems, integrated with sustainability assessments based on ReCiPe 2016 and the GSK Solvent Sustainability Guide. Machine learning models—including a multi-task polynomial regression network, a temperature-adjusted solubility predictor, and a binary solvent model—were combined with Monte Carlo uncertainty quantification to enable a robust solvent recommendation. The entire workflow is encapsulated in SolECOs, an interactive GUI that supports model execution, solubility curve visualization, and dynamic sustainability analysis. A case study on cytarabine demonstrated the platform’s value under different cooling profiles and sustainability priorities. The model consistently identified 1,2-dimethylbenzene as a primary solvent, with polar co-solvents like dichloromethane and ethanol enhancing solubility. However, high predicted solubility did not always align with favorable environmental scores, underscoring the need to balance performance and sustainability. Case studies on four APIs, including cytarabine, under varying cooling gradients and sustainability priorities validated the model’s accuracy and robustness, further demonstrating SolECOs’s ability to support reliable, goal-oriented, and sustainability-aware solvent selection.

High-efficiency and cost-effective photovoltaic technologies have driven great interest in advanced ultra-thin contact structures and their increasing demands, such as in TOPCon solar cells. The present work aims to develop ultra-thin boron-doped polysilicon (p⁺ poly-Si) passivated contacts to minimize optical losses while maintaining superb electrical performance. Ultra-thin poly-Si layers (<40 nm) were prepared by LPCVD, and a systematic study was conducted to optimize key parameters such as boron diffusion temperature and forming gas annealing profiles, etc. Comprehensive structural and optical characterization revealed a pronounced thickness-dependent behaviour in p⁺ poly-Si layers. Thinner p⁺ poly-Si layers (<10 nm) remain quasi-amorphous and substrate-dominated, intermediate layers (<20 nm) show partial crystallization, and thicker layers (~25 to 40 nm) exhibit well-developed polycrystalline structure with excellent passivation (J₀ ≈ 1.3 to 2 fA/cm²), high carrier lifetime (>2000 µs), and low contact resistivity (~ 1 to 1.5 mΩ.cm²). Results of this work outperform or match the literature despite pushing the p⁺ poly-Si to its thinnest level, and provide a path to developing advanced TOPCon solar cells with higher efficiency. However, this will not be easy, as we have to consider the optical perspective as well as overall stability, whether UV or thermal, for these ultra-thin p⁺ poly-Si passivated contacts to integrate them into prototype TOPCon solar cells.

Co-free Li-rich layered oxides are promising cathodes for high-energy lithium-ion batteries, but their practical use is limited by structural degradation, voltage decay, and poor cycling stability at high rates. Here, we investigate microwave-assisted coupling with reduced graphene oxide (rGO) as a strategy to stabilize a V-doped Co-free Li-rich layered cathode, Li_1.2Ni_0.3Mn_0.49V_0.01O₂, using a composite containing 1 wt% rGO. Thermal analysis showed that 1 mol% V does not significantly modify the formation range of the layered phase, supporting calcination at 800 C for 12 h. Raman spectroscopy and X-ray diffraction confirmed the preservation of the layered framework after both Li_1.2Ni_0.3Mn_0.49V_0.01O₂ and microwave-assisted rGO coupling. Rietveld refinement revealed very similar lattice parameters for the Li_1.2Ni_0.3Mn_0.49V_0.01O₂ and Li_1.2Ni_0.3Mn_0.49V_0.01O₂@rOG samples, indicating that the rGO treatment does not induce structural collapse. A low but measurable amount of Ni in the Li layer was detected in both samples, consistent with moderate antisite disorder. Electrochemically, V doping alone improved the high-rate response but reduced cycling stability. After microwave-assisted anchoring with 1 wt% rGO, the composite recovered the lost stability while maintaining strong rate capability. The Li_1.2Ni_0.3Mn_0.49V_0.01O₂@rGO cathode delivered 84.85% discharge-capacity retention after 80 cycles, compared with 79.63% for the Li_1.2Ni_0.3Mn_0.49V_0.01O₂ sample, and achieved discharge capacities of 148.47 and 113.58 mAh g^-1 at 5C and 10C, respectively. These results show that microwave-assisted rGO coupling is an effective route for stabilizing a V-doped Co-free Li-rich layered cathode under demanding rate conditions. Vanadium improves bulk kinetic behavior, whereas rGO enhances interfacial charge transfer and electronic connectivity, thereby improving cycling stability and enabling high discharge capacities at 5C and 10C.

Bismuth-based compounds (Bi₂X₃, where X = O, S, Se, Te) are promising materials for optoelectronic and solar energy applications. They have the potential to replace toxic lead in hybrid perovskites [1-3]. Hybrid lead perovskites (MAPbI₃), when degraded, can release toxic Pb²⁺ into the environment, posing serious risks to human health and ecosystems. To overcome this, lead-based perovskites can be replaced with bismuth-based chalcohalides (MABiXI₂, X = O, S, Se, Te), which offer similar properties with lower toxicity and improved stability. Bismuth chalcohalides can be synthesized using bismuth chalcogenides. In this study, we discuss the synthesis and optoelectronic characterizations of bismuth chalcogenide Bi₂X₃ nanoparticles. They were synthesized via a simple solvothermal method and characterized using structural, optical, and electrical techniques. X-ray diffraction confirmed high-purity nanoparticles with hexagonal, orthorhombic, or tetragonal crystal structures and good crystallinity. Crystallite sizes, calculated using the Scherrer equation, ranged from 15 to 20 nm. Raman spectroscopy revealed distinct vibrational modes corresponding to each phase. Temperature-dependent resistivity measurements confirmed semiconducting behavior, with Bi₂Te₃ showing the highest resistivity and activation energy. The direct band gaps of bismuth chalcogenides were measured in the range of 1.7 eV to 2.9 eV. Electrical analysis based on the Poole–Frenkel model provided insights into the energy barrier that electric charge carriers have to cross to move in the material and also the dielectric constant. These results highlight the potential of Bi₂X₃ nanoparticles in optoelectronic and solar energy devices.