Sustainable energy harvesting from acidic wastewater through a COF-stabilized aramid nanofiber composite membrane

http://english.qibebt.cas.cn/ne/rp/202504/t20250407_909473.html

https://pubs.acs.org/doi/10.1021/jacs.4c18730

A research team from the CAS Qingdao Institute of Bioenergy and Bioprocess Technology (QIBEBT) has introduced a novel membrane design that mimics biological protein channels to enhance proton transport for efficient energy harvesting. Inspired by the ClC-ec1 antiporter found in Escherichia coli, which facilitates the movement of chloride (Cl⁻) and protons, the researchers developed a hybrid membrane composed of covalent organic frameworks (COFs) integrated with aramid nanofibers (ANFs). This ANF/COF composite forms a robust hydrogen-bonding network and features amide groups that selectively bind to Cl⁻ ions, significantly lowering the energy barrier for proton conduction.

In acidic environments, adding just 0.1% Cl⁻ ions (relative to protons) increased the membrane’s proton permeation rate threefold, reaching 9.8 mol m⁻² h⁻¹ for the efficient migration of H⁺ ions. Under simulated acidic wastewater conditions, the ANF/COF membrane achieved an output power density of 434.8 W m⁻²—one of the highest reported to date for osmotic energy generation. It also showed structural stability over 9,000 minutes (~150 hours) of operation in highly acidic media.

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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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