Filtern
Dokumenttyp
Sprache
- Englisch (3)
Referierte Publikation
- ja (3)
Schlagworte
- Catalysis (1)
- Concrete (1)
- Core-shell (1)
- Hackathon (1)
- Halogenation (1)
- Inverse Design (1)
- Large Language model (1)
- Nanoparticles (1)
- Orchestration (1)
- Prediction (1)
- Synthetic metabolism (1)
Organisationseinheit der BAM
The pressing need for novel bioproduction approaches faces a limitation in the number and type of molecules accessed through synthetic biology. Halogenation is widely used for tuning physicochemical properties of molecules and polymers, but traditional halogenation chemistry often lacks specificity and generates harmful by-products. Here, we pose that deploying synthetic metabolism tailored for biohalogenation represents an unique opportunity towards economically attractive and environmentally friendly organohalide production. On this background, we discuss growth-coupled selection of functional metabolic modules that harness the rich repertoire of biosynthetic and biodegradation capabilities of environmental bacteria for in vivo biohalogenation. By rationally combining these approaches, the chemical landscape of Living cells can accommodate bioproduction of added-value organohalides which, as of today, are obtained by traditional chemistry.
Large-language models (LLMs) such as GPT-4 caught the interest of many scientists. Recent studies suggested that these models could be useful in chemistry and materials science. To explore these possibilities, we organized a hackathon. This article chronicles the projects built as part of this hackathon. Participants employed LLMs for various applications, including predicting properties of molecules and materials, designing novel interfaces for tools, extracting knowledge from unstructured data, and developing new educational applications. The diverse topics and the fact that working prototypes could be generated in less than two days highlight that LLMs will profoundly impact the future of our fields. The rich collection of ideas and projects also indicates that the applications of LLMs are not limited to materials science and chemistry but offer potential benefits to a wide range of scientific disciplines.
A facile and efficient methodology is described for the solvothermal synthesis of size-tunable, stable, and uniform NiCu core–shell nanoparticles (NPs) for application in catalysis. The diameter of the NPs is tuned in a range from 6 nm to 30 nm and to adjust the Ni:Cu ratio from 30:1 to 1:1. Furthermore, the influence of different reaction parameters on the final NPs is studied. The NPs are structurally characterized by a method combination of transmission electron microscopy, anomalous small-angle X-ray scattering, X-ray absorption fine structure, and X-ray photoelectron spectroscopy. Using these analytical methods, it is possible to elucidate a core–shell–shell structure of all particles and their chemical composition. In all cases, a depletion from the core to the shell is observed, with the core consisting of NiCu alloy, surrounded by an inner Ni-rich shell and an outer NiO shell. The SiO2-supported NiCu core–shell NPs show pronounced selectivity of >99% for CO in the catalytic reduction of CO2 to CO using hydrogen as reactant (reverse water–gas shift reaction) independent of size and Ni:Cu ratio.