6.7 Materialsynthese und Design
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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.
In this contribution, we present our Self-Driving Lab (SDL) for Nano and Advanced Materials, that integrates robotics for batched autonomous synthesis – from molecular precursors to fully purified nanomaterials – with automated characterization and data analysis, for a complete and reliable nanomaterial synthesis workflow. By fully automating the processing 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 and flexibility of the platform. The system also exhibits high modularity and adaptability in terms of reaction scales and incorporates in-line characterization measurement of hydrodynamic diameter, zeta potential, and optical properties (absorbance, fluorescence). We discuss the excellent reproducibility of the various materials synthesized on the platform in terms of particle size and size distribution, and the adaptability and modularity that allows access to a diverse set of nanomaterial classes.
We also present several key aspects of the central backend that orchestrates the (parallelized) syntheses workflows. One key feature is the resource management or “traffic control” for scheduling and executing parallel reactions in a multi-threaded environment. Another is the interface with data analysis algorithms from in-line, at-line, and off-line measurements. Here, we will give examples of how automatic image segmentation of electron microscopy images with the help of AI can be used for reducing 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 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 common 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.
Nano- and advanced materials are recognized as key enabling technologies of the 21st century, offering exceptional potential to drive innovation and tackle pressing challenges in materials science. To fully realize this potential, it is essential to develop and improve tools that accelerate their design, development, and optimization.
Recognizing this pressing need, we present a Self-Driving Lab (SDL) that integrates robotics for batched autonomous synthesis – from molecular precursors to fully purified nanomaterials, with automated characterization and data analysis, for a complete and reliable nanomaterial synthesis workflow. By fully automating these three process 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 and flexibility of the platform. The system also exhibits modularity, impressive adaptability in terms of reactions scales and incorporates in-line characterization measurement of hydrodynamic diameter, zeta potential, and optical properties (absorbance, fluorescence) of the nanomaterials, along with automating data analysis of at-line or off-line characterization techniques such as electron microscopy image analyses.
Automated characterization and data analysis is complemented by a machine learning–driven feedback loop employing active learning algorithms (e.g., Bayesian optimization, artificial neural networks, and downhill simplex methods) to iteratively suggest new experimental parameters toward desired material properties.
Therefore, the excellent reproducibility for material syntheses when run on the SDL platform multiple times, the material agnostic behavior, the adaptability, and modularity, underscore the SDL’s reliability and potential as a transformative tool for advancing the development and applications of nano- and advanced materials, offering solutions for a sustainable future.
Nano- and advanced materials are recognized as key enabling technologies of the 21st century, offering exceptional potential to drive innovation and tackle pressing challenges in materials science [1]. To fully realize this potential, it is essential to develop and improve tools that accelerate their design, development, and optimization.
Recognizing this pressing need, we present a Self-Driving Lab (SDL) [2] that integrates robotics for batched autonomous synthesis – from molecular precursors to fully purified nanomaterials, with automated characterization and data analysis, for a complete and reliable nanomaterial synthesis workflow. By fully automating these three process 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 and flexibility of the platform. The system also exhibits modularity, impressive adaptability in terms of reactions scales and incorporates in-line characterization measurement of hydrodynamic diameter, zeta potential, and optical properties (absorbance, fluorescence) of the nanomaterials, along with automating data analysis of at-line or off-line characterization techniques such as electron microscopy image analyses [3].
Automated characterization and data analysis is complemented by a machine learning–driven feedback loop employing active learning algorithms (e.g., Bayesian optimization, artificial neural networks, and downhill simplex methods) to iteratively suggest new experimental parameters toward desired material properties.
Therefore, the excellent reproducibility for material syntheses when run on the SDL platform multiple times, the material agnostic behavior, the adaptability, and modularity, underscore the SDL’s reliability and potential as a transformative tool for advancing the development and applications of nano- and advanced materials, offering solutions for a sustainable future.
We present our SDL "Minerva" and its' Orchestrator "Minerva-OS". We discuss the general architecture of the orchestrator, the problems our orchestration solution solves, associated objectives, and success criteria. We demonstrate how a user would interact with it and give examples of what it has already been used for. We also provide some explanation of the available and planned features, and how workflows/experiments are represented. Lastly, we discuss key technical challenges we faced during development.
Melanoma is one of the most aggressive skin cancers and requires innovative therapeutic strategies to overcome the limitations of conventional therapies. In this work, upconversion nanoparticles coated with mesoporous silica and functionalized with folic acid (UCNP@mSiO2-FA) were developed as a targeted nanocarrier system for the delivery of doxorubicin (DOX). The UCNPs were synthesized via thermal decomposition, coated with mesoporous silica shells, and functionalized with folic acid (FA) to enable receptor-mediated targeting. DOX was then loaded into the mesoporous silica coating by adsorption, yielding UCNP@mSiO2-FA-DOX. The different UCNPs were characterized for size, composition, colloidal stability, and loading and release of DOX. This comprehensive physicochemical characterization confirmed a high DOX loading efficiency and a slightly increased drug release under acidic conditions, mimicking the tumour microenvironment. In vitro assays using four melanoma cell lines (A375, B16-F10, MNT-1, and SK-MEL-28) revealed an excellent biocompatibility of UCNP@mSiO2-FA and a significantly higher cytotoxicity of UCNP@mSiO2-FA-DOX compared to unloaded UCNPs, in a dose-dependent manner. Cell cycle analysis demonstrated G2/M phase arrest after treatment with UCNP@mSiO2-FA-DOX, confirming its antiproliferative effect. Overall, UCNP@mSiO2-FA-DOX represents a promising nanoplatform for targeted melanoma therapy, combining active tumour targeting and enhanced anticancer efficacy.
AbstractTwo‐photon polymerization (2PP) additive manufacturing (AM) utilizes feedstocks of ceramic nanoparticles of a few nanometers in diameter, enabling the fabrication of highly accurate technical ceramic design with structural details as small as 500 nm. The performance of these materials is expected to differ from conventional AM ceramics, as nanoparticles and three‐dimensional printing at high resolution introduce new microstructural aspects. This study applies 2PP‐AM of yttria‐stabilized zirconia to investigate the mechanical response behavior under compressive load, probing the influence of smallest structural units induced by the line packing during the printing process, design of sintered microblocks, and sintering temperature and thereby microstructure. We find a dissipative mechanical response enhanced by sintering at lower temperatures than conventional. The pursued 2PP‐AM approach yields a microstructured material with an increased number of grain boundaries that proposedly play a major role in facilitating energy dissipation within the here printed ceramic material. This microplastic response is further triggered by the filigree structures induced by hollow line packing at the order of the critical defect size of ceramics. Together, these unique aspects made accessible by the 2PP‐AM approach contribute to a heterogeneous nano‐ and microstructure, and hint toward opportunities for tailoring the mechanical response in future ceramic applications.
With the increasing production of renewable energy and concomitant depletion of fossil resources, the demand for efficient water splitting electrocatalysts continues to grow. Iridium (Ir) and iridium oxides (IrOₓ) are currently the most promising candidates for an efficient oxygen evolution reaction (OER) in acidic medium, which remains the bottleneck in water electrolysis. Yet, the extremely high costs for Ir hamper a widespread production of hydrogen (H₂) on an industrial scale. Herein, the authors report a concept for the synthesis of electrode coatings with template-controlled mesoporosity surface-modified with highly active Ir species. The improved utilization of noble metal species relies on the synthesis of soft-templated metal oxide supports and a subsequent shape-conformal deposition of Ir species via atomic layer deposition (ALD) at two different reaction temperatures. The study reveals that a minimum Ir content in the mesoporous titania-based support is mandatory to provide a sufficient electrical bulk conductivity. After ALD, a significantly enhanced OER activity results in dependency of the ALD cycle number and temperature. The most active developed electrocatalyst film achieves an outstanding mass-specific activity of 2622 mA mg(Ir)⁻¹ at 1.60 V(RHE) in a rotating-disc electrode (RDE) setup at 25 °C using 0.5 m H₂SO₄ as a supporting electrolyte.
The performance of functional coatings relies strongly on the stability of the polymer-metal interface. The increasing utilization of multi-material structures in the automotive and aerospace industry necessitates a fundamental understanding of the processes leading to interface degradation for the development of novel strategies to increase corrosion and delamination resistance.
The aim of this project is to investigate the corrosion processes at the buried interface of thin film coated aluminium alloy AA2024-T3 under corrosive and coupled corrosive-mechanical load. A spin coating procedure was developed to synthesize epoxy-like coatings and their nanofiller loaded composites with controlled thickness by layer-by-layer deposition of poly[(o-cresyl glycidyl ether)-co-formaldehyde] and poly-(ethylenimine) bi-layers. Our results indicate that the incorporation of graphene into the epoxy-based coatings leads to the improvement of mechanical and barrier properties. Furthermore, the functional groups play important roles in the interfacial bonding between polymer matrix and the nanofillers. Atomic force microscopy (AFM) results indicate very homogeneous and dense films with a thickness of ~25 nm per bi-layer and the successful integration of the nanofillers into the composite coatings. Ellipsometry measurements of film thickness verified a proportional film growth with the number of deposited layers.
The degradation and delamination behavior of the coating systems was characterized by means of in situ AFM corrosion experiments. Complementary energy dispersive X-ray spectroscopy (EDX) analysis was used to correlate the corrosion and delamination behavior with the different intermetallic particle chemistries and distributions. The presentation will summarize our results on the effect of coating composition and build-up on the local corrosion processes on thin film covered AA2024-T3 aluminium alloys.
The paper addresses the “Functional coatings for mechanical applications: characterization and standardization” and the following points are discussed in more detail:
1. Mechanic alapplications (coatings for tools and components, system features vs. material parameters)
2. Mechanical characterization (instrumented indentation testing (IIT), centrifugal adhesion testing (CAT), state of standardization)
3. Topometric characterization (white light interference microscopy (WLIM, 3D), mechanical stylus (MS, 2D), state of standardization)
4. Optical characterization (spectroscopic ellipsometry (SE), inter-laboratory comparison, state of standardization)