QIBEBT-led consortium achieves bacterial degradation of PET bottles to provide terephtalic acid in 97% yield

A research team from the Qingdao Institute of Bioenergy and Bioprocess Technology of the Chinese Academy of Sciences, in collaboration with Nanjing Tech University and Greifswald University, has introduced an innovative solution for the depolymerization of polyethylene terephthalate (PET). This solution utilizes an engineered whole-cell biocatalyst based on the thermophilic bacterium Clostridium thermocellum.

This study builds on prior work, where the research team first demonstrated the concept of whole-cell catalytic PET depolymerization. In that study, the genetically engineered C. thermocellum expressed leaf compost cutinase (LCC) via a plasmid for high-temperature PET depolymerization.

In this study, the researchers integrated LCC directly into the chromosome of C. thermocellum, ensuring stable enzyme expression. They further enhanced the system by introducing LCC variants and co-expressing hydrophobic modules.

By optimizing reaction conditions and controlling pH, the researchers achieved a significant improvement in PET depolymerization efficiency with minimal accumulation of the intermediate product mono(2-hydroxyethyl) terephthalate (MHET).

When tested with pretreated PET bottle particles, about 97% of the added PET was converted into terephthalic acid (TPA), a key monomer used in producing new plastics or high-value chemicals. This high level of performance positions the system as a promising green solution for PET recycling.

Additionally, C. thermocellum is naturally capable of degrading cellulose, making it a potential candidate for directly processing mixed textile waste that contains cotton fibers and PET.

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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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