BioNEMS/MEMS has emerged as an innovative technology for the miniaturisation of biomedical devices with high precision and rapid processing since its first R&D breakthrough in the 1980s. To date, several organic including food waste derived nanomaterials and inorganic nanomaterials (e.g., carbon nanotubes, graphene, silica, gold, and magnetic nanoparticles) have steered the development of high-throughput and sensitive bioNEMS/MEMS-based biosensors, actuator systems, drug delivery systems and implantable/wearable sensors with desirable biomedical properties. Turning food waste into valuable nanomaterials is potential groundbreaking research in this growing field of bioMEMS/NEMS. This review aspires to communicate recent progress in organic and inorganic nanomaterials based bioNEMS/MEMS for biomedical applications, comprehensively discussing nanomaterials criteria and their prospects as ideal tools for biomedical devices. We discuss clinical applications for diagnostic, monitoring, and therapeutic applications as well as the technological potential for cell manipulation (i.e., sorting, separation, and patterning technology). In addition, current in vitro and in vivo assessments of promising nanomaterials-based biomedical devices will be discussed in this review. Finally, this review also looked at the most recent state-of-the-art knowledge on Internet of Things (IoT) applications such as nanosensors, nanoantennas, nanoprocessors, and nanobattery.

Flexible, screen-printed electrode systems offer significant advantages in biomedical applications due to their rapid fabrication capability and adaptability to irregular surfaces. Despite this, no flexible, screen-printed biosensors for pH measurement are readily available on the market. This paper introduces an innovative fabrication technique for integrating flexible screen-printed electrode (SPE) systems with multi-wall carbon nanotubes (MWCNTs). These SPE systems feature flexible screen-printed working electrodes functionalized with f-MWCNT for pH sensing, achieving a fabrication success rate of 78.57%. Furthermore, these SPE systems have been optimized to improve voltage changes and thoroughly characterized for performance using pH buffer solutions. This was done utilizing open-circuit potentiometry. The results demonstrate that the SPE systems can detect pH ranges from 4.0 to 8.9 with an average sensitivity of 33.66 mV/pH, an output range of 202.62 mV, a settling time of 19.71 s, and stability for up to 72 hours. Additionally, the SPE systems have been tested with artificial saliva within the pH range of 5.0 to 8.3, achieving a sensitivity of 44.86 mV/pH and an output range of 192.90 mV. These findings pave the way for further exploration in pH sensing research, particularly utilizing SPE systems coated with MWCNTs.

The aim of this study was to tackle the topic of appropriate recommendations for artificial-saliva and mouthwash usage. The contact angle, pH, and conductivity of two artificial saliva solutions, four mouthwashes, and their mixtures on enamel, glass-ionomer, and composite dental materials were measured. The measurements were conducted with a MATLAB algorithm to minimize human error. The obtained values for the contact angle were in the range from 7.98° to 52.6°, and they showed completely nonlinear and nonuniform behavior for all investigated liquids and on all investigated substrates. Results reveal statistically significant differences among all tested liquids on all investigated substrates (p < 0.05). pH values ranged from 1.54 to 7.01. A wide range of conductivity values were observed, from 1205µS/cm in the saliva-stimulating solution to 6679 mS/cm in the artificial saliva. Spearman’s test showed a moderate positive correlation between the pH and conductivity of the tested fluids (R = 0.7108). A comparison of the data obtained using Image J software and the MATLAB algorithm showed consistency, not exceeding 5% error. When an experiment uses human material and bioactive materials THAT are used in biomedicine as substrates, an additional definition of protocols is highly recommended for future research on this topic.

Textile-based microfluidic biosensors represent an innovative fusion of various multidisciplinary fields, including bioelectronics, material sciences, and microfluidics. Their potential in biomedicine is significant as they leverage textiles to achieve high demands of biocompatibility with the human body and conform to the irregular surfaces of the body. In the field of microfluidics, fabric coated with hydrophobic materials serves as channels through which liquids are transferred in precise amounts to the sensing element, which in this case is a biosensor. This paper presents a condensed overview of the current developments in textile-based microfluidics and biosensors in biomedical applications over the past 20 years (2005–2024). A literature search was performed using the Scopus database. The fabrication techniques and materials used are discussed in this paper, as these will be key in various modifications and advancements in textile-based microfluidics. Furthermore, we also address the gaps in the application of textile-based microfluidic analytical devices in biomedicine and discuss the potential solutions. Advances in textile-based microfluidics are enabled by various printing and fabric manufacturing techniques, such as screen printing, embroidery, and weaving. Integration of these devices into everyday clothing holds promise for future vital sign monitoring, such as glucose, albumin, lactate, and ion levels, as well as early detection of hereditary diseases through gene detection. Although most testing currently takes place in a laboratory or controlled environment, this field is rapidly evolving and pushing the boundaries of biomedicine, improving the quality of human life.

Following the circular economy concept, all areas of science, including electronics, require individual approach. The green electronics approach implies processing natural based materials for development of electronic components even for single use, so that waste does not exist. This paper describes the production of thermistors from 3 different types of edible materials, their testing in laboratory conditions, as well as the application for measuring the temperature in the human oral cavity. To make the thermistor, three types of natural materials were used (carrageenan, polyvinyl alcohol and apple pomace-based). After fabrication, materials were poured into molds. Aluminium foil contacts were placed so that the electrical characterization of the materials could be performed. Several parameters were measured: resistance (R), impedance (Z) and the phase angle (θ) as a function of frequency. In order to determine whether the thermistors have a positive or negative temperature coefficient and to determine their sensitivity, measurements were conducted at three different temperatures: 25 ℃ (room temperature), 37 ℃ (human body temperature), and 50 ℃. The thermistors demonstrated negative temperature coefficient behaviour. Additionally, it was demonstrated that the apple pomace-based thermistor can successfully measure healthy volunteer's temperature. Graphical Abstract

Facial expression muscles serve a fundamental role in the orofacial system, significantly influencing the overall health and well-being of an individual. They are essential for performing basic functions such as speech, chewing, and swallowing. The purpose of this study was to determine whether surface electromyography could be used to evaluate the health, function, or dysfunction of three facial muscles by measuring their electrical activity in healthy people. Additionally, to ascertain whether pattern recognition and artificial intelligence may be used for tasks that differ from one another. The study included 24 participants and examined three muscles (m. Orbicularis Oris, m. Zygomaticus Major, and m. Mentalis) during five different facial expressions. Prior to thorough statistical analysis, features were extracted from the acquired electromyographs. Finally, classification was done with the use of logistic regression, random forest classifier and linear discriminant analysis. A statistically significant difference in muscle activity amplitudes was demonstrated between muscles, enabling the tracking of individual muscle activity for diagnostic and therapeutic purposes. Additionally other time domain and frequency domain features were analyzed, showing statistical significance in differentiation between muscles as well. Examples of pattern recognition showed promising avenues for further research and development. Surface electromyography is a useful method for assessing the function of facial expression muscles, significantly contributing to the diagnosis and treatment of oral motor function disorders. Results of this study show potential for further research and development in this field of research.