A new Fischer-Tropsch route which eliminates CO2 formation

https://en.people.cn/n3/2025/1031/c90000-20384954.html

https://www.science.org/doi/10.1126/science.aea0774

A group at Peking University has developed technology that almost completely eliminates carbon dioxide by-products during Fischer-Tropsch synthesis (FTS), offering a new route to green syngas conversion and low-carbon chemical manufacturing. FTS converts the syngas of carbon dioxide and hydrogen into liquid fuels or high-value chemicals such as olefins. It serves as the pivotal bridge for turning coal, natural gas, biomass and other carbon resources into fuels and value-added chemicals.

The researchers have used a sodium-modified FeC_x@Fe₃O₄ core-shell catalyst coupling water-gas shift (WGS) with syngas-to-olefins (STO) to convert water into hydrogen in situ. HAE reaches about 66 to 83%, exceeding that of methanol-to-olefins (MTO, 50% upper limit). The approximately 95% carbon monoxide conversion and >75% olefin selectivity were simultaneously obtained. The coupling effect was validated by isotope tracing with deuterium oxide and blocking the WGS pathway, and the contribution of WGS was quantitatively evaluated. These results, using lower hydrogen to carbon monoxide ratios, implied that reducing steam consumption in the WGS reaction and reducing the overall output of carbon dioxide and wastewater enabled a sustainable STO process for potential industrialization.

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A humanoid robot training center in Qingdao improves robot efficiency

https://en.people.cn/n3/2026/0324/c90000-20439477.html

At the Qingdao Humanoid Robot Data Training Center in Laoshan District of Qingdao, humanoid robots are trained for jobs such as intelligent industrial manufacturing, smart home, and commercial services. Data collectors here control robots to complete specific tasks like logistics sorting, supermarket restocking, kitchen operations, and component assembly. Through thousands of repetitions and trials, massive amounts of motion data are generated, endowing robots with a smarter “intelligent brain,” and helping humanoid robots enter all walks of life to serve thousands of households.

RamEx: high-throughput microbial ramanome analyses with accurate quality assessment

https://www.cas.cn/syky/202602/t20260226_5102870.shtml

https://doi.org/10.1186/s40168-026-02339-3

A research team at the CAS Qingdao Institute of Bioenergy and Bioprocess Technology has developed RamEx, an integrated analysis framework for Ramanome big data. This platform, tailored to the characteristics of Raman spectroscopy data, establishes a one-stop workflow from data reading and standardized preprocessing to downstream data mining, centered on automated quality control algorithms and efficient parallel computing processes. It also demonstrates a systematic analysis of microbial metabolomical heterogeneity and metabolic pattern differentiation at the single-cell level.

Raman genomics deep analysis can track the dynamic changes in the composition of macromolecules such as lipids, proteins, and nucleic acids in different cells, thus revealing the differentiation and succession patterns of microbial metabolic states at the population scale with single-cell precision. This provides new research ideas and technical pathways for understanding the functional organization and environmental adaptation mechanisms of complex communities.

RamEx removes spectral noise in Raman-based microbial cell sorting

http://english.cas.cn/newsroom/research-news/202602/t20260224_1151116.shtml

https://link.springer.com/article/10.1186/s40168-026-02339-3

Scientists from the CAS Qingdao Institute of Bioenergy and Bioprocess Technology have developed a novel computational tool, RamEx, designed to resolve the computational bottleneck in high-throughput microbial Ramanomics.

RamEx streamlines the full Ramanomic analysis pipeline, from data preprocessing and automated quality control to advanced data mining. An Iterative Convolutional Outlier Detection (ICOD) algorithm tackles spectral noise in an unsupervised manner to dynamically identify and eliminate spectral artifacts, ensuring high-quality input for downstream analysis.

The platform’s performance was validated using diverse datasets, including pathogenic bacteria, probiotics, and yeast fermentation systems. Notably, RamEx successfully captured phenotypic heterogeneity in genetically identical yeast cells by detecting subtle metabolic fluctuations and tracking the dynamic accumulation of intracellular macromolecules, including lipids, proteins, and nucleic acids.

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