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Micromachines
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Medical Internet of Things: From Biosensor Devices, System to Artificial...
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Medical Internet of Things: From Biosensor Devices, System to Artificial Intelligence

A special issue of Micromachines (ISSN 2072-666X).

Deadline for manuscript submissions: closed (15 September 2018) | Viewed by 10190

Special Issue Editors

Prof. Dr. Jinhong Guo

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Guest Editor
School of Communication and Information Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China
Interests: Medical Internet of Things; biosensor; sensory information processing; bioinformatics
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Hejun Du

E-Mail Website
Guest Editor
School of Mechanical and Aerospace Engineering, Nanyang Technological University, Nanyang Avenue, Singapore 639798, Singapore
Interests: MEMS; microfludics; sensors and actuators; smart materials and structures; computational mechanics
Dr. Jun Zhang

E-Mail Website
Guest Editor
School of Mechanical Engineering, Nanjing University of Science and Technology, Xiaolingwei Street No. 200, XuanWu, Nanjing 210094, China
Interests: inertial microfluidics; microparticle/cell manipulation and separation; dielectrophoresis; magnetophoresis; viscoelastic microfluidics
Prof. Dr. Xing Ma

E-Mail Website
Guest Editor
School of Materials Science and Engineering, Harbin Institute of Technology (Shenzhen), Shenzhen 518055, China
Interests: micro/nano-motors; intelligent micro/nanomachine; enzyme catalysis; biosensors; multifunctional nanoparticles; target drug delivery
Special Issues, Collections and Topics in MDPI journals
Dr. Huaying Chen

E-Mail Website
Guest Editor
School of Mechanical Engineering and Automation, Harbin Institute of Technology (Shenzhen), Shenzhen 518005, China
Interests: microfluidics; single cell analysis; paper microfluidics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Nowadays, the market of healthcare is experiencing rapid growth and is believed to be dramatically expanding due to upcoming global aging. For many global health problems, effective treatments already exist. However, limited access to diagnostic equipment often results in late diagnosis and delayed treatment. The medical Internet of Things (IoT) is endowed with the expectation to fulfil the rigid demand, which require that all kinds of digital medical devices link and access the Internet to acquire real-time parameters related to personalized health, such as medical images, and biochemical and biophysical parameters. Family doctors or medical Artificial Intelligence (AI) systems can access the related biomedical information of their patients and give precise, personalized and preventive healthcare consultations. Medical data acquisition is the basis for the construction of medical IoT. The proposed issue will cover all aspects of medical IoT, from on-chip biological sample preparation to medical sensor development, biomedical sensor network, big health data processing and AI application in medicine, which is capable of providing a systematic view on the medical IoT. The success of the proposed issue will have a significant impact on the connected healthcare industry and market, since big healthcare is a hot topic with great research efforts and fast development. We believe this work will also attract many professional audiences in industry.  This Micromachines Special Issue on “Medical Internet of Things: From Biosensor Devices, System to Artificial Intelligence” intends to collect the most relevant original short communications, review articles, and research papers, from industry and academia. We welcome your contribution and we would also appreciate your referral to a colleague who might be interested.

Prof. Dr. Jinhong Guo
Prof. Dr. Hejun Du
Dr. Jun Zhang
Prof. Dr. Xing Ma
Dr. Huaying Chen
Guest Editors

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 100 words) can be sent to the Editorial Office for announcement on this website.

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. Micromachines is an international peer-reviewed open access monthly 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 and lab chips
  • sensors
  • biosensors
  • point of care system
  • medical internet of things
  • digital health processing

Published Papers (2 papers)

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Research

10 pages, 1259 KiB  
Article
Photolithographic Patterning of Cytop with Limited Contact Angle Degradation
by Yalei Qiu, Shu Yang and Kuang Sheng
Micromachines 2018, 9(10), 509; https://doi.org/10.3390/mi9100509 - 09 Oct 2018
Cited by 5 | Viewed by 4421
Abstract
Cytop is a commercially available amorphous fluoropolymer with excellent characteristics including electric insulation, water and oil repellency, chemical resistance, and moisture-proof property, making it an attractive material as hydrophobic layers in electrowetting-on-dielectric (EWOD) devices. However, its highly hydrophobic surface makes it difficult for [...] Read more.
Cytop is a commercially available amorphous fluoropolymer with excellent characteristics including electric insulation, water and oil repellency, chemical resistance, and moisture-proof property, making it an attractive material as hydrophobic layers in electrowetting-on-dielectric (EWOD) devices. However, its highly hydrophobic surface makes it difficult for photoresists to be directly coated on the surface. To pattern Cytop, plasma treatment prior to applying photoresists is required to promote the adhesion between the photoresist and the Cytop coating. This approach inevitably causes hydrophobicity loss in the final EWOD devices. Thus, a damage-reduced recipe for Cytop patterning is urgently needed. In this paper, we first characterized the damage caused by two categories of surface treatment methods: plasma treatment and metal treatment. Parameters such as plasma gas source (Ar/O2), plasma treatment time (0–600 s), metal target (Al/Cu/Cr/Au), metal deposition process (magnetron sputtering or e-beam evaporation) were varied. Film thickness, wettability, and roughness were quantified by ellipsometry measurements, contact angle measurements, and atom force microscope (AFM), respectively. We then evaluated the effectiveness of annealing in damage reduction. Experimental results show that: (1) annealing is necessary in restoring hydrophobicity as well as smoothing surfaces; (2) specified film thickness can be obtained by controlling plasma treatment time; (3) “Ar/O2 plasma treatment + an AZ5214 soft mask + annealing” is a feasible recipe; (4) “an Al/Cu/Cr/Au hard mask + annealing” is feasible as well. Full article
(This article belongs to the Special Issue Medical Internet of Things: From Biosensor Devices, System to Artificial Intelligence)
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9 pages, 3136 KiB  
Article
On-Chip Facile Preparation of Monodisperse Resorcinol Formaldehyde (RF) Resin Microspheres
by Jianmei Wang, Xiaowen Huang, Pei Zhao, Xueying Wang, Ye Tian, Chengmin Chen, Jianchun Wang, Yan Li, Wei Wan, Hanmei Tian, Min Xu, Chengyang Wang and Liqiu Wang
Micromachines 2018, 9(1), 24; https://doi.org/10.3390/mi9010024 - 12 Jan 2018
Cited by 3 | Viewed by 5102
Abstract
Monodisperse resorcinol formaldehyde resin (RF) microspheres are an important polymeric material because of their rich surface functional group and uniform structural characteristics and have been increasingly applied as an electrode material, catalyst support, absorbent, and carbon microsphere precursor. The polymerization conditions, such as [...] Read more.
Monodisperse resorcinol formaldehyde resin (RF) microspheres are an important polymeric material because of their rich surface functional group and uniform structural characteristics and have been increasingly applied as an electrode material, catalyst support, absorbent, and carbon microsphere precursor. The polymerization conditions, such as the gelation/solidification temperature and the residence time, can largely influence the physical properties and the formation of the 3D polymeric network of the RF microspheres as well as the carbon microspheres. However, few studies have reported on the complexity of the gelation and solidification processes of resol. In this work, we developed a new RF microsphere preparation device that contains three units: a droplet generation unit, a curing unit, and a collection unit. In this system, we controlled the gelation and solidification processes of the resol and observed its curing behavior, which helped us to uncover the curing mechanism of resol. Finally, we obtained the optimized polymerization parameters, obtaining uniform RF microspheres with a variation coefficient of 4.94%. The prepared porous RF microspheres presented a high absorption ability, reaching ~90% at 10 min. Thus, our method demonstrated the practicality of on-chip monodisperse microspheres synthesis. The product was useful in drug delivery and adsorbing large poisonous molecules. Full article
(This article belongs to the Special Issue Medical Internet of Things: From Biosensor Devices, System to Artificial Intelligence)
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