Soft Microsystems

A special issue of Micromachines (ISSN 2072-666X). This special issue belongs to the section "A:Physics".

Deadline for manuscript submissions: closed (30 October 2020) | Viewed by 16631

Special Issue Editor


E-Mail Website
Guest Editor
School of Biological and Health Systems Engineering, Arizona State University, Tempe, AZ 85287-9709, USA
Interests: neural interfaces; neuromodulation; bioMEMS

Special Issue Information

Dear Colleagues,

Physically soft microsystems are being increasingly recognized as being significant for applications such as robotics and implantable microsystems. In the case of implantable microsystems, several studies have shown improved long-term performance of neural interfaces when the mismatch in elastic modulus between the implant material and surrounding soft tissue is reduced. Soft materials and corresponding fabrication processes have the potential, therefore, to impact a range of implantable sensors and devices that modulate function(s) of biological tissue. However, challenges exist in identifying soft materials that will lend themselves to microscale systems and also in developing corresponding batch fabrication processes that are reliable, consistent, and high-yield. This Special Issue will focus on the design, development, and testing of novel soft microsystems that involve flexible, soft substrates and microscale interfaces as their defining functional feature with applications in robotics, implantable systems, etc. Also within the scope of this Special Issue are studies that focus on addressing cellular and molecular mechanisms underlying interactions between soft microscale implants and the surrounding tissue under long-term conditions.

Prof. Jit Muthuswamy
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 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

  • Flexible substrates
  • Soft interfaces
  • Soft robotics
  • Implants
  • Neural interfaces
  • Sensors
  • Actuators
  • Flexible electronics

Published Papers (5 papers)

Order results
Result details
Select all
Export citation of selected articles as:

Research

Jump to: Review

23 pages, 7265 KiB  
Article
Soft, Conductive, Brain-Like, Coatings at Tips of Microelectrodes Improve Electrical Stability under Chronic, In Vivo Conditions
by Arati Sridharan and Jit Muthuswamy
Micromachines 2021, 12(7), 761; https://doi.org/10.3390/mi12070761 - 28 Jun 2021
Cited by 8 | Viewed by 2364
Abstract
Several recent studies have reported improved histological and electrophysiological outcomes with soft neural interfaces that have elastic moduli ranging from 10 s of kPa to hundreds of MPa. However, many of these soft interfaces use custom fabrication processes. We test the hypothesis that [...] Read more.
Several recent studies have reported improved histological and electrophysiological outcomes with soft neural interfaces that have elastic moduli ranging from 10 s of kPa to hundreds of MPa. However, many of these soft interfaces use custom fabrication processes. We test the hypothesis that a readily adoptable fabrication process for only coating the tips of microelectrodes with soft brain-like (elastic modulus of ~5 kPa) material improves the long-term electrical performance of neural interfaces. Conventional tungsten microelectrodes (n = 9 with soft coatings and n = 6 uncoated controls) and Pt/Ir microelectrodes (n = 16 with soft coatings) were implanted in six animals for durations ranging from 5 weeks to over 1 year in a subset of rats. Electrochemical impedance spectroscopy was used to assess the quality of the brain tissue–electrode interface under chronic conditions. Neural recordings were assessed for unit activity and signal quality. Electrodes with soft, silicone coatings showed relatively stable electrical impedance characteristics over 6 weeks to >1 year compared to the uncoated control electrodes. Single unit activity recorded by coated electrodes showed larger peak-to-peak amplitudes and increased number of detectable neurons compared to uncoated controls over 6–7 weeks. We demonstrate the feasibility of using a readily translatable process to create brain-like soft interfaces that can potentially overcome variable performance associated with chronic rigid neural interfaces. Full article
(This article belongs to the Special Issue Soft Microsystems)
Show Figures

Figure 1

10 pages, 2387 KiB  
Article
Simplified Method of Microcontact Force Measurement by Using Micropressure Sensor
by Huamin Zhu, Fuzhong Zheng, Huiwen Leng, Cheng Zhang, Kun Luo, Yibo Cao and Xing Yang
Micromachines 2021, 12(5), 515; https://doi.org/10.3390/mi12050515 - 04 May 2021
Cited by 2 | Viewed by 1568
Abstract
Microcontact force measurement is widely applied in micro/nano manufacturing, medicine and microelectromechanical systems. Most microcontact force measurements are performed by using mass comparators, nano-indenter and precision electronic balance, and weighing sensors. However, these instruments have a complex structure and high cost. Nevertheless, the [...] Read more.
Microcontact force measurement is widely applied in micro/nano manufacturing, medicine and microelectromechanical systems. Most microcontact force measurements are performed by using mass comparators, nano-indenter and precision electronic balance, and weighing sensors. However, these instruments have a complex structure and high cost. Nevertheless, the rapid development of microsensor technology provides a new, simple and low-cost approach for microcontact force measurement. In this study, we present a method of microcontact force measurement by using micropressure sensors and study the relationship amongst the microcontact force, output voltage and contact position of the sensor. We use a microcapacitance pressure sensor as an example, then we perform a simulation calculation and construct a microcontact force experiment system to verify the simulation results. The experimental and simulation results are consistent. In addition, an equation that describes the relationship amongst the microcontact force, output voltage and contact position of the sensor is obtained. Based on this simple and low-cost method, we build a micro-manipulation system, which indicates that the micropressure sensors can be used to measure microcontact force in various applications easily and cost-effectively. Furthermore, it is considerably relevant to research and application in this field. Full article
(This article belongs to the Special Issue Soft Microsystems)
Show Figures

Figure 1

19 pages, 8366 KiB  
Article
Computational and Histological Analyses for Investigating Mechanical Interaction of Thermally Drawn Fiber Implants with Brain Tissue
by Kanghyeon Kim, Changhoon Sung, Jungjoon Lee, Joonhee Won, Woojin Jeon, Seungbeom Seo, Kyungho Yoon and Seongjun Park
Micromachines 2021, 12(4), 394; https://doi.org/10.3390/mi12040394 - 02 Apr 2021
Cited by 4 | Viewed by 2801
Abstract
The development of a compliant neural probe is necessary to achieve chronic implantation with minimal signal loss. Although fiber-based neural probes fabricated by the thermal drawing process have been proposed as a solution, their long-term effect on the brain has not been thoroughly [...] Read more.
The development of a compliant neural probe is necessary to achieve chronic implantation with minimal signal loss. Although fiber-based neural probes fabricated by the thermal drawing process have been proposed as a solution, their long-term effect on the brain has not been thoroughly investigated. Here, we examined the mechanical interaction of thermally drawn fiber implants with neural tissue through computational and histological analyses. Specifically, finite element analysis and immunohistochemistry were conducted to evaluate the biocompatibility of various fiber implants made with different base materials (steel, silica, polycarbonate, and hydrogel). Moreover, the effects of the coefficient of friction and geometric factors including aspect ratio and the shape of the cross-section on the strain were investigated with the finite element model. As a result, we observed that the fiber implants fabricated with extremely softer material such as hydrogel exhibited significantly lower strain distribution and elicited a reduced immune response. In addition, the implants with higher coefficient of friction (COF) and/or circular cross-sections showed a lower strain distribution and smaller critical volume. This work suggests the materials and design factors that need to be carefully considered to develop future fiber-based neural probes to minimize mechanical invasiveness. Full article
(This article belongs to the Special Issue Soft Microsystems)
Show Figures

Figure 1

24 pages, 2095 KiB  
Article
Investigating the Association between Motor Function, Neuroinflammation, and Recording Metrics in the Performance of Intracortical Microelectrode Implanted in Motor Cortex
by Evon S. Ereifej, Youjun Li, Monika Goss-Varley, Youjoung Kim, Seth M. Meade, Keying Chen, Jacob Rayyan, He Feng, Keith Dona, Justin McMahon, Dawn Taylor, Jeffrey R. Capadona and Jiayang Sun
Micromachines 2020, 11(9), 838; https://doi.org/10.3390/mi11090838 - 03 Sep 2020
Cited by 1 | Viewed by 2124
Abstract
Long-term reliability of intracortical microelectrodes remains a challenge for increased acceptance and deployment. There are conflicting reports comparing measurements associated with recording quality with postmortem histology, in attempts to better understand failure of intracortical microelectrodes (IMEs). Our group has recently introduced the assessment [...] Read more.
Long-term reliability of intracortical microelectrodes remains a challenge for increased acceptance and deployment. There are conflicting reports comparing measurements associated with recording quality with postmortem histology, in attempts to better understand failure of intracortical microelectrodes (IMEs). Our group has recently introduced the assessment of motor behavior tasks as another metric to evaluate the effects of IME implantation. We hypothesized that adding the third dimension to our analysis, functional behavior testing, could provide substantial insight on the health of the tissue, success of surgery/implantation, and the long-term performance of the implanted device. Here we present our novel analysis scheme including: (1) the use of numerical formal concept analysis (nFCA) and (2) a regression analysis utilizing modern model/variable selection. The analyses found complimentary relationships between the variables. The histological variables for glial cell activation had associations between each other, as well as the neuronal density around the electrode interface. The neuronal density had associations to the electrophysiological recordings and some of the motor behavior metrics analyzed. The novel analyses presented herein describe a valuable tool that can be utilized to assess and understand relationships between diverse variables being investigated. These models can be applied to a wide range of ongoing investigations utilizing various devices and therapeutics. Full article
(This article belongs to the Special Issue Soft Microsystems)
Show Figures

Figure 1

Review

Jump to: Research

25 pages, 1919 KiB  
Review
The Role of Soft Robotic Micromachines in the Future of Medical Devices and Personalized Medicine
by Lourdes Garcia, Genevieve Kerns, Kaitlin O’Reilley, Omolola Okesanjo, Jacob Lozano, Jairaj Narendran, Conor Broeking, Xiaoxiao Ma, Hannah Thompson, Preston Njapa Njeuha, Drashti Sikligar, Reed Brockstein and Holly M. Golecki
Micromachines 2022, 13(1), 28; https://doi.org/10.3390/mi13010028 - 26 Dec 2021
Cited by 22 | Viewed by 7161
Abstract
Developments in medical device design result in advances in wearable technologies, minimally invasive surgical techniques, and patient-specific approaches to medicine. In this review, we analyze the trajectory of biomedical and engineering approaches to soft robotics for healthcare applications. We review current literature across [...] Read more.
Developments in medical device design result in advances in wearable technologies, minimally invasive surgical techniques, and patient-specific approaches to medicine. In this review, we analyze the trajectory of biomedical and engineering approaches to soft robotics for healthcare applications. We review current literature across spatial scales and biocompatibility, focusing on engineering done at the biotic-abiotic interface. From traditional techniques for robot design to advances in tunable material chemistry, we look broadly at the field for opportunities to advance healthcare solutions in the future. We present an extracellular matrix-based robotic actuator and propose how biomaterials and proteins may influence the future of medical device design. Full article
(This article belongs to the Special Issue Soft Microsystems)
Show Figures

Figure 1

Back to TopTop