Integrated Sample Processing and Counting Microfluidic Device for Microplastics Analysis.
ANALYTICA CHIMICA ACTA(2023)
Beijing Univ Chem Technol
Abstract
BACKGROUND:The presence of microplastics is widespread in the ocean, freshwater, soil, or even in the human body. The current microplastics analysis method involves a relatively complicated sieving, digestion filtration, and manual counting process, which is both time-consuming and requires experienced operation personnel.RESULT:This study proposed an integrated microfluidic approach for the quantification of microplastics from river water sediment and biosamples. The proposed two-layer PMMA-based microfluidic device is able to conduct the sample digestion, filtration and counting processes inside the microfluidic chip with the preprogrammed sequence. For demonstration, samples from river water sediment and fish gastrointestinal tract were analyzed, result indicate the proposed microfluidic device is able to perform the quantification of microplastics from river water and biosamples.SIGNIFICANCE AND NOVELTY:Compared with the conventional approach, the proposed microfluidic-based sample processing and quantification method for microplastics are simple, low-cost and with low demand for laboratory equipments, the self-contained system also has the application potential for the continuous on-site inspection of microplastics.
MoreTranslated text
Key words
Microplastics,Microfluidics,Digestion,Filtration,Nile red
求助PDF
上传PDF
View via Publisher
AI Read Science
AI Summary
AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.
Example
Background
Key content
Introduction
Methods
Results
Related work
Fund
Key content
- Pretraining has recently greatly promoted the development of natural language processing (NLP)
- We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
- We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
- The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
- Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance
Upload PDF to Generate Summary
Must-Reading Tree
Example

Generate MRT to find the research sequence of this paper
Data Disclaimer
The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn
Chat Paper
Summary is being generated by the instructions you defined