ArticleEnvironmental science & technology2024
Introducing ARTiMiS: A Low-Cost Flow Imaging Microscope for Microalgal Monitoring.
Article in Environmental science & technology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
3 citing papers in PubMed.
- Development of a low-cost real-time monitoring system for biomass concentration and environmental factors in microalgae Limnospira fusiformis cultivation.Bioprocess and biosystems engineering · 2026Article
- Automatic detection of harmful cyanobacterial genera using deep CNN models and artemisinin optimization.Scientific reports · 2025Article
- Integrating Machine Learning with Flow-Imaging Microscopy for Automated Monitoring of Algal Blooms.Environmental science & technology · 2025Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Manual microscopy is the gold standard for phytoplankton monitoring in diverse engineered and natural environments. However, it is both labor-intensive and requires specialized training for accuracy and consistency, and therefore difficult to implement on a routine basis without significant time investment. Automation can reduce this burden by simplifying the measurement to a single indicator (e.g., chlorophyll fluorescence) measurable by a probe, or by processing samples on an automated cytometer for more granular information. The cost of commercially available flow imaging cytometers, however, poses a steep financial barrier to adoption. To overcome these labor and cost barriers, we developed ARTiMiS: the Autonomous Real-Time Microbial 'Scope. The ARTiMiS is a low-cost flow imaging microscopy-based platform with onboard software capable of providing taxonomically resolved quantitation of phytoplankton communities in real-time. ARTiMiS leverages novel multimodal imaging and onboard machine learning-based data processing that is currently optimized for a curated and expandable database of industrially relevant microalgae. We demonstrate its operational limits, performance in identification of laboratory-cultivated microalgae, and potential for continuous monitoring of complex microalgal communities in full-scale industrial cultivation systems.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.