1 year birthday party of “Green Carbon” with a 2024 National Green Carbon Science Conference

http://qibebt.cas.cn/news/zyxw/202410/t20241017_7402455.html

From October 17 – 19, the first birthday of the journal was celebrated with a conference to which over 100 scientists attended.

Signing ceremony of the Qingdao Synthetic Biology Technology Innovation Strategic Alliance

T the conference theme was “Innovation of green and low-carbon technology, empowering carbon peak and carbon neutrality”, focusing on carbon resources, low-carbon conversion technology, multi-energy integration and utilization and other fields for in-depth discussions. The conference invited more than 100 experts, scholars, and business people to attend the conference.

As the chairman of this conference, QIBEBT director and Green Carbon editor-in-chief Lv Xuefeng said that this conference aims to build a high-level exchange platform for scientific research in the field of green and low-carbon research across the country. Breakthroughs will be sought in perovskite photovoltaics, solid-state lithium batteries, biosynthesis of energetic materials, green bio-jet fuel, new generation of bio-based green plasticizers, and iron-branched butyl-pentane rubber. In order to further expand China’s academic influence in the field of green and low-carbon science and technology, the international academic journal Green Carbon aims to promote scientific and technological innovation in the field of sustainable development and provide a high-quality and open academic exchange platform for global researchers in the field of green and low-carbon.

At the meeting, the “Qingdao Synthetic Biology Technology Innovation Strategic Alliance” jointly established by the Qingdao Institute of Energy and relevant enterprises, universities and research institutes was formally established. Qingdao Wanyuan Environmental Technology Co., Ltd., Shandong Jinzhirui New Materials Development Co., Ltd., Shandong Environmental Protection Development Group Co., Ltd., Qingdao Zhongchuanghuike Biotechnology Co., Ltd., Qingdao Zhongke Green Carbon Technology Co., Ltd., Shandong Hengxin Group Co., Ltd., Qingdao Zhongke Yuanben New Energy Co., Ltd., and China Hydrogen Energy Company (Energy Cube (Qingdao) Data Technology Co., Ltd.) signed contracts with the Institute respectively.

The conference also held a special event for the first anniversary of the publication of the international journal Green Carbon. Jiang Lei and Lv Xuefeng presented the “Excellent Editorial Board Award” to Rolf Schmid, Tang Yong, Chen Xuesi and Lv Xuefeng presented the “Excellent Young Editorial Board Award” to 24 young editors, and Rolf Schmid and Lv Xuefeng jointly unveiled the Green Carbon Overseas (Germany) Office.

more insights

Single-cell ramanomics improve quality control in P34HB biopolymer industrial fermentation

http://english.cas.cn/newsroom/research-news/202604/t20260423_1157877.shtml

https://doi.org/10.1016/j.tibtech.2026.03.017

A team led by the CAS Qingdao Institute of Bioenergy and Bioprocess Technology (QIBEBT) has developed a new “process ramanomics” platform. This technology enables real-time, data-driven control of biomanufacturing.

The researchers validated their approach in polyhydroxyalkanoate (PHA) fermentation, a key route for biodegradable polyesters used in packaging and medical materials. Powered by machine learning, the platform achieved 99.75% accuracy in distinguishing PHB-producing cells from P34HB-producing ones, and quantified total PHA content and monomer composition at the single-cell level with a median absolute deviation below 3.8%, comparable to traditional gas chromatography.

In a pivotal 5,000-liter industrial fermenter trial, traditional offline testing pointed to harvesting at 28 hours when PHA content registered 66.32%. Process ramanomics, however, revealed a compositional shift invisible to conventional methods: the 4HB monomer ratio was 8.67% at 26 hours (within specification) but climbed to 11.28% by 28 hours, exceeding the compliance limit, demonstrating that earlier termination could safeguard product quality.

The platform’s single-cell resolution also showed that the content of intracellular PHA can vary by more than threefold among individual cells. At 26 hours, population heterogeneity was lowest, with 91.54% of cells producing at high levels and a 4HB composition that was within specification. This confirmed that 26 hours was the optimal harvest window.

The scientists further showed that process ramanomics can be applied to different chassis organisms and products. For example, it can be used for protein synthesis in yeast and lipid synthesis in Rhodococcus. This suggests that process ramanomics could serve as a general-purpose analytics engine for next-generation intelligent bioreactors.

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.

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