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Paper des Monats
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Machine-learning interatomic potentials are widely used as computationally efficient surrogates for density functional theory in atomistic simulations, enabling large-scale, long-time modeling of materials systems. We investigate how different fine-tuning strategies influence the prediction of harmonic phonon band structures, thermal properties, and the potential energy surface along imaginary phonon modes. We achieve substantial accuracy improvements with minimal additional data, with as few as 10 additional training structures already yielding significant gains. In addition to existing approaches, we introduce Equitrain, a finetuning framework that implements LoRA-based adaptation. Across 53 materials systems, we show that fine-tuned models consistently outperform both the underlying pretrained model and models trained from scratch. Equitrain achieves the best overall performance, and our results demonstrate that fine-tuning enables accurate phonon predictions.
Artificial intelligence is transforming molecular and materials science, but its growing computational and data demands raise critical sustainability challenges. In this Perspective, we examine resource considerations across the AI-driven discovery pipeline--from quantum-mechanical (QM) data generation and model training to automated, self-driving research workflows--building on discussions from the ``SusML workshop: Towards sustainable exploration of chemical spaces with machine learning'' held in Dresden, Germany. In this context, the availability of large quantum datasets has enabled rigorous benchmarking and rapid methodological progress, while also incurring substantial energy and infrastructure costs. We highlight emerging strategies to enhance efficiency, including general-purpose machine learning (ML) models, multi-fidelity approaches, model distillation, and active learning. Moreover, incorporating physics-based constraints within hierarchical workflows, where fast ML surrogates are applied broadly and high-accuracy QM methods are used selectively, can further optimize resource use without compromising reliability. Equally important is bridging the gap between idealized computational predictions and real-world conditions by accounting for synthesizability and multi-objective design criteria, which is essential for practical impact. Finally, we argue that sustainable progress will rely on open data and models, reusable workflows, and domain-specific AI systems that maximize scientific value per unit of computation, enabling efficient and responsible discovery of technological materials and therapeutics.
We present analytically convoluted expressions for models commonly used in small-angle X-ray scatteringv(SAXS), specifically for measurements made with instruments operating in line-focus (Kratky-type) or Ultra-SAXS (Bonse-Hart) geometries. Starting with Guinier's approximation for determining the radii of gyration, we discuss power laws, random-coil conformations of macromolecules, and spherical nanoparticles. We provide expressions for the Guinier-Porod model, the Teubner-Strey model (for microemulsions), and the generalized Ornstein-Zernike model (for gels and nanogels1). An example of the Teubner-Strey model is provided. The line-convoluted models discussed here are intended for interpreting data that has not been deconvoluted (i.e., not "desmeared"). Using line-convoluted data allows for lower detection limits and shorter measurement times compared to deconvoluted data, as artifacts introduced by deconvolution can be avoided by applying these models. We suggest utilizing line-convoluted models for high-throughput in-house SAXS analysis, which could enable time-saving routine examinations. For instance, SAXS investigations could help determine whether certain materials should be classified as nanomaterials under the European Commission's regulatory.
Evaluating the performance of biopolyol-based rigid foams derived from rice straw liquefaction
(2026)
Purpose
The polyurethane sector primarily relies on petrochemical substances, including polyols and isocyanates. Given the swift consumption of fossil fuel resources and the rising concerns about ecological issues and global warming, this study aims to explore the sustainable advancement of polyurethane rigid foam by using renewable biopolyols derived from agricultural waste liquefaction.
Design/methodology/approach
The liquefaction of lignocellulosic biomass involves breaking down complex polymers into smaller molecules using heat, chemicals and catalysts to prepare biopolyol as a renewable feedstock for the polyurethane industry. Spectral analysis of the liquefaction products verified that the process achieved the desired outcome and indicated the presence of hydroxyl groups. The biopolyol analysis demonstrated a biomass conversion rate of up to 87% and a hydroxyl number between 230 and 250 mg KOH/g, suggesting that this biopolyol could serve as a viable alternative to petrochemical polyols.
Findings
Various formulations of biopolyol obtained from rice straw liquefaction, conducted at 160 °C for 2 h, were prepared. Intensive study was conducted on the applicability of using biopolyol in rigid foam refrigerator formulation in comparison to petroleum counterparts. The results obtained from scanning electron microscopy showed that the biopolyol-based foams had a symmetrical cell structure and a significant proportion of sealed cells. Biobased foam demonstrated superior thermal insulation compared to its petrochemical-based equivalent.
Originality/value
These results underscore the feasibility of agricultural waste liquefaction as an eco-friendly approach for synthesizing biopolyols and their application in polyurethane foam production. The study contributes to the development of sustainable materials in the polymer industry and supports the transition toward renewable feedstocks in rigid foam applications. The study, moreover, introduces PEG 400 as a novel liquefaction solvent, offering improved compatibility with rigid polyurethane systems and establishing a new benchmark for sustainable rigid foam production.
Preparation workflows of fluorescent nm- and µm-sized polymer particles used as reporters in fluorescent assays, bioimaging, and sensing studies or calibration tools for fluorescence methods in the life sciences can be time-consuming and labor-intensive. Also, the outcome can be operator dependent. Here, we present simple and cost-efficient automated workflows for dye loading and surface labeling of polystyrene particles (PSP), using a commercial self-programmable pipetting robot. For developing and fine-tuning automated staining workflows, hydrophobic Nile Red (NR) was incorporated into 100 nm, 200 nm, and 1000 nm PSP by an adapted swelling procedure of premanufactured particles in the presence of hydrophobic dyes, thereby confirming its reliability and versatility. Subsequent expansion of our automation concept to the labeling of carboxylated PSP with pH-sensitive 6-aminofluorescein (6-AMF) and aminated PSP with pH-responsive fluorescein isocyanate (FITC) demonstrated its broad applicability. All automated workflows were optimized and validated by gravimetry and spectroscopic measurements with a microtiter plate (MTP) reader in absorption and fluorescence mode to ensure particle recovery and reproducible fluorescence features and determine PSP dye loading and labeling efficiencies. Comparison with the manual fabrication of NR-stained PSP using an established swelling protocol showed that our automation approach utilizing a pipetting robot considerably reduced the variability in particle recovery and dye loading efficiency. Overall, our simple, labor- and time-efficient workflows with inexpensive and broadly available commercial automation tools present attractive alternatives to manual particle loading and labeling and provide the basis for fast parameter screening, parallel processing, and decreased hands-on time.
In the Seminar "Capacity building and Knowledge Exchange in Research Management" following three points are presented in detail:
- Example of a successfully completed international project,
- Role of institutional support in project success, and
- Practical challenges and lessons learned from a coordinator‘s perspective.
Challenges in nanoplastic analysis: Reference materials, complex matrices, and limits of detection
(2026)
The growing concern over nanoplastic particles (< 1 µm) in food and the environment has intensified the need for robust analytical approaches to detect, quantify, and characterize these particles. But the lack is high of standardized and well-characterized reference materials that reflect environmentally relevant nanoplastics according to:
• Size distribution
• Low abundance
• Chemical diversity
• Surface properties
• Behavior in complex matrices.
This poster shows different methods like FFF and AF4-MALS and their results when measuring a nanoPP reference materials candidate.
The lack of nanoplastic (NPs) reference materials capable of mimicking real-world scenarios is currently hampering the development of validated extraction techniques from complex matrices, the development of analytical approaches for robust identification and quantification, and the ability to use environmentally relevant test materials in (eco)toxicity testing and hazard assessment.
Here, we present a strategy towards the development of a potential NP reference material based on nano-sized polyproplyene particles (nanoPP) developed within the EURAMET-funded PlasticTrace project (https://plastictrace.eu/). The strategy includes the preparation, the physicochemical characterization and the stability assessment of nanoPP in suspension both in its pristine state, but also when spiked into complex matrices like mineral water and milk.
NanoPP was produced reproducibly in a top-down approach by crushing in acetone with an UltraTurrax, filtering and change of solvent to MilliQ water. The prepared nanoPP material was subjected to a comprehensive physicochemical characterization including e.g., DLS, PTA and AF4-MALS for particle size distribution assessment, PTA and SEM for particle number concentration determination, SEM and AFM for shape analysis and pyrolysis GC-MS for chemical identification. Performed studies showed that the prepared nanoPP material is irregularly shaped and highly polydisperse with a particle size of around 180 nm (e.g., Dh,z-ave from DLS) and a particle size distribution from around 15 nm up to 135 nm (radius of gyration Rg from AF4-MALS).
Storage stability studies using DLS, PTA and AF4-MALS revealed nanoPP in aqueous suspension to be stable for at least 22 months with respect to particle size distribution and number concentration rendering it a promising NP candidate reference material. However, conducted spiking experiments in mineral water and milk showed a time-dependent agglomeration of nanoPP that needs further investigation.
Supramolecular systems exhibit collective functions that emerge from the hierarchical assembly of individual molecular building blocks. Metal−organic polyhedra (MOPs) are a class of functional supramolecular architectures with well-defined molecular geometry and an intrinsic cavity. In recent years, considerable progress has been made in assembling MOPs into extended networks to create porous solids. However, most reported MOP assemblies are limited to three-dimensional networks, which, owing to their high-dimensional connectivity, hinder effective stress dissipation and render them brittle under mechanical stress.
Here, we report the one-dimensional (1D) self-assembly of MOPs into supramolecular polymeric aerogels that combine permanent microporosity with exceptional mechanical flexibility. The reaction between amino acid-functionalized naphthalenediimide (NDI) linkers and copper salts led to the synthesis of octahedral copper-based MOPs, followed by their spontaneous self-assembly to form supramolecular gels. The corresponding aerogels converted from the gels possessed uniform 1D fibrillar networks (14.8 ± 2.2 nm in width) with intrinsic microporosity derived from individual MOP cavities. The aerogel endured 87% compressive strain without fracture, demonstrating distinct ductility. Furthermore, these fibrils in the supramolecular gels were converted to crystals after 2 weeks. Single-crystal X-ray diffraction revealed that MOPs are arranged via face-to-face interaction between octahedral MOPs to form a 1D helical chain structure. An analysis of the self-assembly process using Hansen solubility parameters unveiled that solvent conditions with high polarity and low dispersion interaction drive the formation of anisotropic assemblies. This work provides a new strategy for tailoring the mechanical properties of supramolecular materials through dimensional control of their assemblies.
The recent emergence of self-driving laboratories (SDL) and material acceleration plat-forms (MAPs) demonstrates the ability of these systems to change the way chemistry and material syntheses will be performed in the future. Especially in conjunction with nano- and advanced materials which are generally recognized for their great potential in solving current material science challenges, such systems can make disrupting con-tributions. Consequently, new tools that enhance the development and optimization cycle of nano- and advanced materials are crucial. In this contribution, we present our Self-Driving Lab (SDL) for Nano and Advanced Materials [1], that integrates robotics for batched autonomous synthesis – from molecular precursors to fully purified nano-materials – with automated characterization and data analysis, for a complete and reli-able nanomaterial synthesis workflow. By automating the processing and characteriza-tion steps for seven different materials from five representative, completely different classes of nano- and advanced materials (metal, metal oxide, silica, metal organic framework, and core–shell particles) that follow different reaction mechanisms, we demonstrate the great versatility, reproducibility, and flexibility of the platform.
The system also incorporates in-line characterization measurement of hydrodynamic diameter, zeta potential, and optical properties (absorbance, fluorescence). In general, the interface with data analysis algorithms from in-line, at-line, and off-line measure-ments is of great importance for closing the design-make-test-analyze cycle and using these platforms efficiently. Here, we will give examples of how automatic image seg-mentation of electron microscopy images with the help of AI [2] can be used for reduc-ing the “data analysis bottleneck” from an off-line measurement. We will also discuss various machine learning (ML) algorithms that are currently implemented in the backend and can be used for ML-guided, closed-loop material optimization in our SDL. Lastly, we will show our recent efforts [3] in making the workflow generation on SDLs more user-friendly by using large language models to generate executable workflows automatically from synthesis procedures given in natural language and user-friendly graphical user interfaces based on node editors that also allow for knowledge graph extraction from the workflows. In this context, we are currently also working on a com-mon description or ontology for representing the process steps and parameters of the workflows, which will greatly facilitate the semantic description and interoperability of workflows between different SDL hardware and software platforms.
These features underscore the SDL’s potential as a transformative tool for advancing and accelerating the development of nano- and advanced materials, offering solutions for a sustainable and environmentally responsible future.