Microbial fuel cells (MFCs) are electrochemical fuel cells that directly convert the chemical energy of organic compounds in biomass into electrical energy. Due to their self-sustainability, direct current output, and fast response, MFC biosensors have the potential for long-term environmental monitoring applications. For the first time, we report a biological memory effect (BME) in MFC biosensors during repeated toxin injections. The toxin response of the biosensors generally weakens over repeated toxin stimuli injection at low concentrations. Experimental results demonstrate that the current drop of the second and third toxin injection is only 48.88% and 28.13%, respectively, of the first toxin injection on average. To investigate this BME, an ordinary differential equation (ODE) model is established. By fitting ODE model parameters to the experimental results, the model successfully simulates the experiments and the BME. This ODE model has good potential to compensate for the BME with its predictive ability, and it may potentially correct inaccuracies that accrue during long-term environmental monitoring for MFC biosensors. The current research paves the way for implementing MFC biosensors for long-term environmental toxic chemical detection.

In this paper, we report the development of a wireless, passive, biocompatible, and flexible system for stimulation of human induced pluripotent stem cell derived cardiomyocytes (hiPSC-CMS). Fabricated on a transparent parylene/PDMS substrate, the proposed stimulator enables real-time excitation and characterization of hiPSC-CMs cultured on-board. The device comprises a rectenna operating at 2.35 GHz which receives radio frequency (RF) energy from an external transmitter and converts it into DC voltage to deliver monophasic stimulation. The operation of the stimulator was primarily verified by delivering monophasic voltage pulses through gold electrodes to hiPSC-CMs cultured on the Matrigel-coated substrates. Stimulated hiPSC-CMs beat in accordance with the monophasic pulses when delivered at 0.5, 1, and 2 Hz pulsing frequency, while no significant cell death was observed. The wireless stimulator could generate monophasic pulses with an amplitude of 8 V at a distance of 15 mm. These results demonstrated the proposed wireless stimulator's efficacy for providing electrical stimulation to engineered cardiac tissues. The proposed stimulator will have a wide application in tissue engineering where a fully wireless stimulation of electroconductive cells is needed. The device also has potential to be employed as a cardiac stimulator by delivering external stimulation and regulating the contractions of cardiac tissue.

Respiratory behavior contains crucial parameters to feature lung functionality, including respiratory rate, profile, and volume. The current well-adopted method to characterize respiratory behavior is spirometry using a spirometer, which is bulky, heavy, expensive, requires a trained provider to operate, and is incapable of continuous monitoring of respiratory behavior, which is often critical to assess chronic respiratory diseases. This work presents a wireless wearable sensor on a paper substrate that is capable of continuous monitoring of respiratory behavior and delivering the clinically relevant respiratory information to a smartphone. The wireless wearable sensor was attached on the midway of the xiphoid process and the costal margin, corresponding to the abdomen-apposed rib cage, based on the anatomical and experimental analysis. The sensor, with a footprint of 40 X 35 X 6 mm3 and weighing 6.5 g, including a 2.7 g battery, consists of three subsystems, (i) ultrasound emitter, (ii) ultrasound receiver, and (iii) data acquisition and wireless transmitter. The sensor converts the linear strain at the wearing site to the lung volume change by measuring the change in ultrasound pressure as a function of the distance between the emitter and the receiver. The temporal lung volume change data, directly converted from the ultrasound pressure, is wirelessly transmitted to a smartphone where a custom-designed app computes to show volume-time and flow rate-volume loop graphs, standard respiratory analysis plots. The app analyzes the plots to show the clinically relevant respiratory behavioral parameters, such as forced vital capacity (FVC) and forced expiratory volume delivered in the first second (FEV1). Potential user-induced error on sensor placement and temperature sensitivity were studied to demonstrate the sensor maintains its performance within a reasonable range of those variables. Eight volunteers were recruited to evaluate the sensor, which showed the mean deviation of the FEV1/FVC ratio in the range of 0.00-4.25% when benchmarked by the spirometer. The continuous measurement of respiratory behavioral parameters helps track the progression of the respiratory diseases, including asthma progression to provide alerts to relevant caregivers to seek needed timely treatment.

Extracellular electron transfer (EET) allows exoelectrogens to transfer electrons outside their outer membrane as a unique process to complete respiration. The EET kinetics of Geobacter sulfurreducens is critical to further improving the performance of microbial electrochemical technologies such as microbial fuel cells. However, the EET of Geobacter sulfurreducens is not yet fully understood, and current and power densities of microbial fuel cells meet with a bottleneck. This work addresses this deficit by studying the kinetic parameters of the EET of Geobacter sulfurreducens enriched microbial community for a series of biofilm growth stages through the non-linear fitting of discharging current profiles of micro-scale microbial fuel cells. The quantitative EET rate constants and the amount of redox cofactors associated with individual EET steps are obtained from initial to fully-grown biofilms. This quantitative study reports the rate-limiting step in EET transitions during biofilm growth. In early to mid-stage biofilms, we report current densities of less than 2.2 Am−2 and between 2.2 and 3.1 Am−2, respectively. The rate-limiting step transitions from irreversible acetate turnover to the electron transfer from inside the exoelectrogen to extracellular redox cofactors (ERCs) within the biofilm. We show fully-grown biofilms have a current density of more than 3.1 Am−2, and the rate-limiting step here is instead the electron transfer from ERCs within the biofilm to ERCs at the anode. The results of this study illuminate the mechanisms of the EET of Geobacter sulfurreducens, using quantitative reaction kinetics parameter analysis.

Biopotential signals contain essential information for assessing functionality of organs and diagnosing diseases. We present a flexible sensor, capable of measuring biopotentials, in real time, in wireless and fully-passive manner. The flexible sensor collects and transmits biopotentials to an external reader without wire, battery, or harvesting/regulating element. The sensor is fabricated on a 90 μm-thick polyimide substrate with footprint of 18 × 15 × 0.5 mm3. The wireless fully-passive acquisition of biopotentials is enabled by the RF (Radio Frequency) microwave backscattering effect where the biopotentials are modulated by an array of varactors with incoming RF carrier that is backscattered to the external reader. The flexile sensor is verified and validated by emulated signal and Electrocardiogram (ECG), Electromyogram (EMG), and Electrooculogram (EOG), respectively. A deep learning algorithm analyzes the signal quality of wirelessly acquired data, along with the data from commercially-available wired sensor counterparts. Wired and wireless data shows <3% discrepancy in deep learning testing accuracy for ECG and EMG up to the wireless distance of 240 mm. Wireless acquisition of EOG further demonstrates accurate tracking of horizontal eye movement with deep learning training and testing accuracy reaching up to 93.6% and 92.2%, respectively, indicating successful detection of biopotentials signal as low as 250 μVPP. These findings support that the real-time wireless fully-passive acquisition of on-body biopotentials is indeed feasible and may find various uses for future clinical research.

Hydrocephalus (HCP) is a chronic neurological brain disorder caused by a malfunction of the cerebrospinal fluid (CSF) drainage mechanism in the brain. The current standard method to treat HCP is a shunt system. Unfortunately, the shunt system suffers from complications including mechanical malfunctions, obstructions, infections, blockage, breakage, overdrainage, and/or underdrainage. Some of these complications may be attributed to the shunts' physically large and lengthy course making them susceptible to external forces, siphoning effects, and risks of infection. Additionally, intracranial catheters artificially traverse the brain and drain the ventricle rather than the subarachnoid space. We report a 3D-printed microelectromechanical system-based implantable valve to improve HCP treatment. This device provides an alternative approach targeting restoration of near-natural CSF dynamics by artificial arachnoid granulations (AGs), natural components for CSF drainage in the brain. The valve, made of hydrogel, aims to regulate the CSF flow between the subarachnoid space and the superior sagittal sinus, in essence, substituting for the obstructed arachnoid granulations. The valve, operating in a fully passive manner, utilizes the hydrogel swelling feature to create nonzero cracking pressure, PT ≈ 47.4 ± 6.8 mmH2O, as well as minimize reverse flow leakage, QO ≈ 0.7 μL/min on benchtop experiments. The additional measurements performed in realistic experimental setups using a fixed sheep brain also deliver comparable results, PT ≈ 113.0 ± 9.8 mmH2O and QO ≈ 3.7 μL/min. In automated loop functional tests, the valve maintains functionality for a maximum of 1536 cycles with the PT variance of 44.5 mmH2O < PT < 61.1 mmH2O and negligible average reverse flow leakage rates of ∼0.3 μL/min.

Respiratory behaviors provide useful measures of lung health. The current methods have limited capabilities of continuous characterization of respiratory behaviors, often required to assess respiratory disorders and diseases. This work presents a system equipped with a machine learning algorithm, capable of continuously monitoring respiratory behaviors. The system, consisting of two wireless wearable sensors, accurately extracts and classifies the features of respiratory behaviors of subjects within various postures, wirelessly transmitting the temporal respiratory behaviors to a laptop. The sensors were attached on the midway of the xiphoid process and the costal margin, and 1 cm above the umbilicus, respectively. The wireless wearable sensor, consisting of ultrasound emitter, ultrasound receiver, data acquisition and wireless transmitter, has a small footprint and light weight. The sensors correlate the mechanical strain at wearing sites to lung volume by measuring the local circumference changes of the chest and abdominal walls simultaneously. Eleven subjects were recruited to evaluate the wireless wearable sensors. Three different random forest classifiers, including generic, individual, and weighted-adaptive classifiers, were used to process the wireless data of the subjects at four different postures. The results demonstrate the respiratory behaviors are individual- and posture-dependent. The generic classifier merely reaches the accuracy of classifying postures of 21.9 ± 1.7% while individual and weighted-adaptive classifiers mark substantially high, up to 98.9 ± 0.6% and 98.8 ± 0.6%, respectively. The accurate monitoring of respiratory behaviors can track the progression of respiratory disorders and diseases, including chronic respiratory obstructive disease (COPD), asthma, apnea, and others for timely and objective approaches for control.

Wireless implantable neural interfaces can record high-resolution neuropotentials without constraining patient movement. Existing wireless systems often require intracranial wires to connect implanted electrodes to an external head stage or/and deploy an application-specific integrated circuit (ASIC), which is battery-powered or externally power-transferred, raising safety concerns such as infection, electronics failure, or heat-induced tissue damage. This work presents a biocompatible, flexible, implantable neural recorder capable of wireless acquisition of neuropotentials without wires, batteries, energy harvesting units, or active electronics. The recorder, fabricated on a thin polyimide substrate, features a small footprint of 9 mm × 8 mm × 0.3 mm and is composed of passive electronic components. The absence of active electronics on the device leads to near zero power consumption, inherently avoiding the catastrophic failure of active electronics. We performed both in vitro validation in a tissue-simulating phantom and in vivo validation in an epileptic rat. The fully passive wireless recorder was implanted under rat scalp to measure neuropotentials from its contact electrodes. The implanted wireless recorder demonstrated its capability to capture low voltage neuropotentials, including somatosensory evoked potentials (SSEPs), and interictal epileptiform discharges (IEDs). Wirelessly recorded SSEP and IED signals were directly compared to those from wired electrodes to demonstrate the efficacy of the wireless data. In addition, a convoluted neural network-based machine learning algorithm successfully achieved IED signal recognition accuracy as high as 100 and 91% in wired and wireless IED data, respectively. These results strongly support the fully passive wireless neural recorder’s capability to measure neuropotentials as low as tens of microvolts. With further improvement, the recorder system presented in this work may find wide applications in future brain machine interface systems.

This paper introduces a deep learning enabled generative sensing framework which integrates low-end sensors with computational intelligence to attain a high recognition accuracy on par with that attained with high-end sensors. The proposed generative sensing framework aims at transforming low-end, low-quality sensor data into higher quality sensor data in terms of achieved classification accuracy. The low-end data can be transformed into higher quality data of the same modality or into data of another modality. Different from existing methods for image generation, the proposed framework is based on discriminative models and targets to maximize the recognition accuracy rather than a similarity measure. This is achieved through the introduction of selective feature regeneration in a deep neural network (DNN). The proposed generative sensing will essentially transform low-quality sensor data into high-quality information for robust perception. Results are presented to illustrate the performance of the proposed framework.

The electrical conductivity of a Geobacter-enriched biofilm anode fed with acetate medium was assessed using small gold electrode systems as substrate concentration was changed. At 1.86 A/m(2) of the steady-state current density, biofilm conductivity was stable at 0.48-0.51 mS/cm. This high biofilm conductivity was not changed at short-term starvation ( < 7 h). In comparison, biofilm conductivity significantly decreased down to 0.16 +/- 0.002 mS/cm after 4 d of long-term starvation (current density 0.03 +/- 0.01 A/m(2)). Biofilm conductivity, however, was recovered to 0.35 +/- 0.03 mS/cm after 25 mM acetate spiking in a few days. The decrease and increase of biofilm conductivity in response to substrate conditions for long-term starvation was consistently observed in three consecutive cycles. This dynamic biofilm conductivity indirectly indicates that catabolism of exoelectrogens (i.e., energy generation) is associated with biofilm conductivity.

Persistent cough is a symptom common to a number of respiratory disorders; however, reliable monitoring of cough frequency and cough severity over an extended period of time can be a challenge. Traditional methods involve subjective evaluation by care providers or patient self-reports. As an alternative, we propose an objective method for monitoring cough using a wearable microphone. We collected 24-hour audio recordings from 9 patients suffering from chronic obstructive pulmonary disease, asthma, and lung cancer using the VitaloJAK wearable microphone. Trained professionals carefully listened to each audio stream and manually labeled each cough event. Using this data, we propose a new neural-network-based cough detection scheme. A pre-processing algorithm is used to estimate the start and end of each cough and the deep neural network is trained using each cough instance. Experiments demonstrate an average leave-one-participant-out cross-validation specificity and sensitivity of 93.7% and 97.6% respectively.

Fuel cells offer attractive solutions in energy converters. Some fuel cells have been used in large scale vehicles, eg, buses, and others are expected to penetrate a variety of markets. Such fuel cells also benefit small-size, low-power demands, such as wireless MEMS (micro-electromechanical systems), including remote sensors, wearable electronics, and environmental monitoring. This chapter introduces miniaturized fuel cells for wireless MEMS, analyzes their technologies, compares their performance parameters, and discusses a specific device, namely the supercapacitor, to utilize miniaturized fuel cell technologies.

We report a low-voltage, yet effective, micro-electromechanical systems (MEMS) structure capable of mitigating external mechanical disturbances, such as a physical shock. External shock onto MEMS devices can be catastrophic as a conventional single membrane may travel beyond stable oscillatory distances under shock and become irreparably damaged. However, the simple addition of a second membrane on top of the single membrane drastically reduces oscillatory distances by electrostatically holding the bottom membrane within stable oscillation. The added elements, in conjunction with a fine-control algorithm, mitigate the impact of a mechanical shock onto the MEMS device. From experimental findings, it is found that the dual-membrane structure effectively reduces the travel distance of the bottom membrane by 41.5%, upon deploying merely 0.565 V onto the additional membrane. The dynamic implementation of the shock mitigation method, using an on-board accelerometer as a trigger, delivered in-situ mitigation of shock on a dual-membrane MEMS structure.

This study assessed the conductivity of a Geobacter-enriched biofilm anode in a microbial electrochemical cell (MxC) equipped with two gold anodes (25 mM acetate medium), as different proton gradients were built throughout the biofilm. There was no pH gradient across the biofilm anode at 100 mM phosphate buffer (current density 2.38 A/m2) and biofilm conductivity (Kbio) was as high as 0.87 mS/cm. In comparison, an inner biofilm became acidic at 2.5 mM phosphate buffer in which dead cells were accumulated at ∼80 μm of the inner biofilm anode. At this low phosphate buffer, Kbio significantly decreased by 0.27 mS/cm, together with declined current density of 0.64 A/m2. This work demonstrates that biofilm conductivity depends on the composition of live and dead cells in the conductive biofilm anode.

A microbial fuel cell (MFC) is a bioinspired energy converter which directly converts biomass into electricity through the catalytic activity of a specific species of bacteria. The effect of temperature on a miniaturized microbial fuel cell with Geobacter sulfurreducens dominated mixed inoculum is investigated in this paper for the first time. The miniaturized MFC warrants investigation due to its small thermal mass, and a customized setup is built for the temperature effect characterization. The experiment demonstrates that the optimal temperature for the miniaturized MFC is 322–326 K (49–53 °C). When the temperature is increased from 294 to 322 K, a remarkable current density improvement of 282% is observed, from 2.2 to 6.2 Am−2. Furthermore, we perform in depth analysis on the effect of temperature on the miniaturized MFC, and found that the activation energy for the current limiting mechanism of the MFC is approximately between 0.132 and 0.146 eV, and the result suggest that the electron transfer between cytochrome c is the limiting process for the miniaturized MFC.

We present a battery-free radio frequency (RF) microwave activated wireless stimulator, 25 × 42 × 1.6 mm3 on a flexible substrate, featuring high current delivery, up to 60 mA, to stimulate engineered cardiac tissues. An external antenna shines 2.4 GHz microwave, which is modulated by an inverted pulse to directly control the stimulating waveform, to the wireless passive stimulator. The stimulator is equipped with an on-board antenna, multistage diode multipliers, and a control transistor. Rat cardiomyocytes, seeded on electrically conductive gelatin-based hydrogels, demonstrate synchronous contractions and Ca2+ transients immediately upon stimulation. Notably, the stimulator output voltage and current profiles match the tissue contraction frequency within 0.5-2 Hz. Overall, our results indicate the promising potential of the proposed wireless passive stimulator for cardiac stimulation and therapy by induction of precisely controlled and synchronous contractions.

The paper report a unique biologically inspired electro-chemical reference electrode based on regulating the breathing of bacteria. Some species of bacteria, named exoelectrogen, have the capability of extracellular electron transfer, which is the transfer of electrons to a solid electron acceptor outside their membrane. We find that it sets the solid electron acceptor at a stable electrochemical potential, which can be used as reference electrode. We pattern thin film platinum as electron acceptor and grow exoelectrogenic biofilm on it. By performing colorimetric analysis of the individual ions in the anolyte solution, we confirmed that the potential of the reference electrode, ∼ −0.5 V versus the Ag/AgCl in 3M NaCl, arises from the electrochemical potential of the reaction. The biologically-inspired reference electrode demonstrates a stability of ±4.2 mV/day for two days. It is integrated in a MEMS microbial fuel cell (MFC) to characterize its electrochemical characteristics.

Goal: We propose a novel wireless fully passive neural recording device for unobtrusive neuropotential monitoring. Previous work demonstrated the feasibility of monitoring emulated brain signals in a wireless fully passive manner. In this paper, we propose a novel realistic recorder that is significantly smaller and much more sensitive. Methods: The proposed recorder utilizes a highly efficient microwave backscattering method and operates without any formal power supply or regulating elements. Also, no intracranial wires or cables are required. In-vitro testing is performed inside a four-layer head phantom (skin, bone, gray matter, and white matter). Results: Compared to our former implementation, the neural recorder proposed in this study has the following improved features: 1) 59% smaller footprint, 2) up to 20-dB improvement in neuropotential detection sensitivity, and 3) encapsulation in biocompatible polymer. Conclusion : For the first time, temporal emulated neuropotentials as low as 63 μVpp can be detected in a wireless fully passive manner. Remarkably, the high-sensitivity achieved in this study implies reading of most neural signals generated by the human brain. Significance: The proposed recorder brings forward transformational possibilities in wireless fully passive neural detection for a very wide range of applications (e.g., epilepsy, Alzheimer's, mental disorders, etc.).