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Intelligent Microfluidics
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Intelligent Microfluidics

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Intelligent Sensors".

Deadline for manuscript submissions: closed (1 April 2026) | Viewed by 1902

Special Issue Editor

Dr. Rasim Guldiken

E-Mail Website
Guest Editor
College of Engineering, Architecture and Technology, Oklahoma State University, 215 N Hester St, Stillwater, OK 74078, USA
Interests: sensors and actuators; microfluidics; ultrasonics; engineering education
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue is dedicated to highlighting the notable advancements in the interdisciplinary field of microfluidics, specifically how artificial intelligence (AI), machine learning (ML), and sensor technologies are transforming microfluidic systems. Microfluidics technology is characterized by the deliberate manipulation of fluids at the submillimeter scale, and it has already brought about significant transformations in numerous sectors, including healthcare, diagnostics, chemical synthesis, and environmental monitoring.

This Special Issue mainly focuses on examining sensor-integrated microfluidic chips for real-time monitoring, AI-enabled microfluidic systems for high-throughput analysis, and ML-based predictive models for microfluidics. The application of AI and ML in microfluidic systems has resulted in significant advancements in microfluidics in recent years. Integrating sensor technologies with microfluidic chips has facilitated the real-time monitoring of chemical reactions, cell culture experiments, and biological assays. AI-enabled microfluidic systems have significantly increased the speed and accuracy of data analysis by automating complex data processing tasks. Additionally, ML-based predictive models have enabled the prediction of the behavior of microfluidic systems under various conditions, thus allowing for more efficient and effective experimentation.

Overall, this Special Issue provides a comprehensive overview of the latest developments in the field of microfluidics and will be of great interest to researchers, scientists, and engineers working on developing microfluidic systems. We welcome contributions that present cutting-edge research and stimulate discussion on the future implications and potential of intelligent microfluidics.

Dr. Rasim Guldiken
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • microfluidics
  • lab-on-a-chip
  • artificial intelligence
  • machine learning
  • bioMEMS
  • point-of-care devices
  • biomedical devices

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Published Papers (2 papers)

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Research

14 pages, 3413 KB  
Article
An Oscillating-Flow Microfluidic PCR Method for Rapid and Flexible Detection of Periodontal Pathogens
by Zhenqing Li, Yueqing Wang, Jing Yang, Bo Yang, Yuan Zeng, Shinichi Sekine and Yoshinori Yamaguchi
Sensors 2026, 26(7), 2126; https://doi.org/10.3390/s26072126 - 29 Mar 2026
Viewed by 528
Abstract
Polymerase chain reaction (PCR) is widely regarded as the gold standard for nucleic acid analysis; however, conventional thermal cycling limits its applicability in rapid and compact analytical systems. Here, we report an oscillating-flow microfluidic PCR method that enables rapid and flexible amplification by [...] Read more.
Polymerase chain reaction (PCR) is widely regarded as the gold standard for nucleic acid analysis; however, conventional thermal cycling limits its applicability in rapid and compact analytical systems. Here, we report an oscillating-flow microfluidic PCR method that enables rapid and flexible amplification by repeatedly shuttling the reaction mixture between two fixed-temperature zones. Unlike continuous-flow PCR, the proposed approach decouples PCR cycle number from microchannel geometry, allowing programmable cycling while reducing chip footprint. To enhance analytical reliability, polymer-assisted surface passivation using polyvinylpyrrolidone was employed to suppress nonspecific adsorption in polydimethylsiloxane (PDMS) microchannels, significantly improving amplification efficiency. Using Porphyromonas gingivalis and Treponema denticola as representative periodontal pathogens, 35-cycle amplification was completed within 20 min with reliable product yield. The proposed method advances oscillating-flow PCR toward a robust analytical strategy for rapid pathogen detection and related microfluidic nucleic acid analysis. Full article
(This article belongs to the Special Issue Intelligent Microfluidics)
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17 pages, 2932 KB  
Article
Label-Free Detection of HeLa Cells Activity Excited by Blue LED
by Vera Gradišnik, Darko Gumbarević and Petar Kolar
Sensors 2026, 26(4), 1294; https://doi.org/10.3390/s26041294 - 17 Feb 2026
Viewed by 545
Abstract
This paper investigates a novel optical method that uses a high-responsivity a-Si:H photodiode for label-free detection of luminescence from HeLa cervical cancer cells excited by a blue LED. We examine the energy distribution of the energy-gap density of states (DOS) from the photodiode’s [...] Read more.
This paper investigates a novel optical method that uses a high-responsivity a-Si:H photodiode for label-free detection of luminescence from HeLa cervical cancer cells excited by a blue LED. We examine the energy distribution of the energy-gap density of states (DOS) from the photodiode’s long-time transient current, which shows exponential decay kinetics in the HeLa cell reaction. We analysed the transient response of a-Si:H p-i-n photodiode upon the illumination of the analyte with a pulsed blue LED light to better understand the HeLa cells activity and the fundamental defect kinetics processes in the a-Si:H material. Results suggest that the characteristic very low-level, time-varying light response of HeLa cells is due to chemiluminescence within cells, resulting from the reaction between nitric oxide (NO) and hydrogen peroxide (H2O2). Given the low signal intensity and noise, we applied a Savitzky–Golay (SG) filter to post-process the data. By reducing noise without attenuating chemiluminescent peaks, the Savitzky–Golay filter enabled accurate, reproducible quantification of the photocurrent response, reflecting the kinetics of cellular reactions. Further studies and more precise measurement instruments are needed for this real-time, label-free, non-destructive method, which applies SG-filtered signal processing to microfluidic optical biosensors. Full article
(This article belongs to the Special Issue Intelligent Microfluidics)
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