Verification of using virtual reality to evaluate deficiencies in cognitive function among patients with schizophrenia in the remission stage: a cross-sectional study.

BACKGROUND: Schizophrenia is associated with widespread cognitive impairment. The MATRICS Consensus Cognitive Battery (MCCB) is most frequently used to assess cognitive function. However, the MCCB test is time consuming for the clinician. Virtual reality (VR) has emerged as an adjunctive tool to overcome this limitation and provides a new means to assess cognitive function. METHODS: The present study examined the validity and safety of using VR technology to assess cognitive function in Han Chinese patients with schizophrenia (SZs). The VR cognition training system (VRCTS) was used to simulate real-life supermarkets and assess cognitive function. Thirty-two SZs and 25 healthy controls (HCs) underwent VRCTS and MCCB assessments. An auxiliary diagnosis model was created based on the outcomes of the VRCTS to classify SZs and HCs by cognitive impairment. RESULTS: Significant differences in completion time between the SZs and HCs were detected using the VRCTS. SZs spent more time completing tasks than HCs. The outcome of VRCTS significantly correlated with the MCCB. The auxiliary diagnosis model had a sensitivity of 88.89% and a specificity of 88.89%. CONCLUSIONS: These results support the use of VR technology in the assessment of cognitive impairment in Han Chinese schizophrenia patients. TRIAL REGISTRATION: China Clinical Trial Registry, ChiVTR1800016121. Registered 13 May 2018, http://www.chictr.org.cn/showproj.aspx?proj=27233.

Accurate Peak Detection for Optical Sensing with Reduced Sampling Rate and Calculation Complexity.

Fiber Bragg gratings (FBGs) are widely applied in optical sensing systems due to their advantages including being simple to use, high sensitivity, and having great potential for integration into optical communication systems. A common method used for FBG sensing systems is wavelength interrogation. The performance of interrogation based sensing systems is significantly determined by the accuracy of the wavelength peak detection processing. Direct maximum value readout (DMVR) is the simplest peak detection method. However, the detection accuracy of DMVR is sensitive to noise and the sampling resolution. Many modified peak detection methods, such as filtering and curve fitting schemes, have been studied in recent decades. Though these methods are less sensitive to noise and have better sensing accuracy at lower sampling resolutions, they also confer increased processing complexity. As massive sensors may be deployed for applications such as the Internet of things (IoT) and artificial intelligence (AI), lower levels of processing complexity are required. In this paper, an efficient scheme applying a three-point peak detection estimator is proposed and studied, which shows a performance that is close to the curve fitting methods along with reduced complexity. A proof-of-concept experiment for temperature sensing is performed. 34% accuracy improvement compared to the DMVR is demonstrated.

Fiber Bragg grating sensing system with wavelength-swept-laser distribution and self-synchronization.

Fiber Bragg gratings (FBGs) with various interrogation schemes to estimate the FBG's spectrum shift have been widely used in fiber sensing systems. Wavelength swept laser (WSL) based interrogation architectures have been proposed to offer rapid and high-quality sensing performance. However, for getting higher sensing accuracy, the demands for high-performance WSL may push the system cost. Under these considerations, a WSL distribution architecture allowing multiple sensing processing units (SPUs) to share the WSL is studied in this Letter. A self-synchronization scheme is proposed to enable flexible SPU deployment with no concerns for the clock calibration. The proposed system is experimentally studied. Temperature estimation error of ∼2.5∘C and ∼0.5∘C with sensitivities of 0.13°C/ms and 0.14°C/ms, respectively, for the high and small temperature ranges are demonstrated.

Universal Triboelectric Nanogenerator Simulation Based on Dynamic Finite Element Method Model.

The lack of a universal simulation method for triboelectric nanogenerator (TENG) makes the device design and optimization difficult before experiment, which protracts the research and development process and hinders the landing of practical TENG applications. The existing electrostatic induction models for TENGs have limitations in simulating TENGs with complex geometries and their dynamic behaviors under practical movements due to the topology change issues. Here, a dynamic finite element method (FEM) model is proposed. The introduction of air buffer layers and the moving mesh method eliminates the topology change issues during practical movement and allows simulation of dynamic and time-varying behaviors of TENGs with complex 2D/3D geometries. Systematic investigations are carried out to optimize the air buffer thickness and mesh densities, and the optimized results show excellent consistency with the experimental data and results based on other existing methods. It also shows that a 3D disk-type rotating TENG can be simulated using the model, clearly demonstrating the capability and superiority of the dynamic FEM model. Moreover, the dynamic FEM model is used to optimize the shape of the tribo-material, which is used as a preliminary example to demonstrate the possibility of designing a TENG-based sensor.

Mode Analysis of Pt/LGS Surface Acoustic Wave Devices.

Platinum (Pt) gratings on langasite (LGS) substrates are a widely used structures in high temperature surface acoustic wave (SAW) devices. Multiple modes can be excited in Pt/LGS SAW devices owing to the heavy weight of the Pt electrode and leaky waves in the LGS substrate. In this work, we report on a detailed mode analysis of Pt/LGS SAW devices, where three commonly used LGS cuts are considered. A three-dimensional (3D) finite element method (FEM) numerical model was developed, and the simulation and experiment results were compared. The experiment and simulation results showed that there are two modes excited in the Pt/LGS SAW devices with Euler angle (0°, 138.5°, 27°) and (0°, 138.5°, 117°), which are Rayleigh-type SAW and SH-type leaky wave, respectively. Only the Rayleigh-type mode was observed in the Pt/LGS SAW devices with Euler angle (0°, 138.5°, 72°). It was found that the acoustic velocities are dependent on the wavelength, which is attributed to the change of wave penetration depth in interdigital transducers (IDTs) and the velocity dispersion can be modulated by the thickness of the Pt electrode. We also demonstrated that addition of an Al2O3 passivation layer has no effect on the wave modes, but can increase the resonant frequencies. This paper provides a better understanding of the acoustic modes of Pt/LGS SAW devices, as well as useful guidance for device design. It is believed that the Rayleigh-type SAW and SH-type leaky waves are potentially useful for dual-mode sensing applications in harsh environments, to achieve multi-parameter monitoring or temperature-compensation on a single chip.

Triboelectric Nanogenerator-Based Self-Powered Resonant Sensor for Non-Destructive Defect Detection.

A triboelectric nanogenerator-based self-powered resonant sensor is proposed and investigated. By integrating an inductor and a microswitch with a triboelectric nanogenerator, a new type triboelectric nanogenerator is obtained, the pulse voltage output is converted to an oscillating signal with a very stable modulated resonant frequency, immune to the cross disturbance of contact-related variation (force, frequency, distance) and environmental variation, such as humidity and temperature. This is utilized for non-destructive defect detection. When the coil inductor scans the surface of a specimen with defects, varying resonant frequencies are obtained for different types of defects, showing excellent consistency between the experimental and simulated results. The results demonstrate the potential of the self-powered TENG-based resonant sensor to be a highly stable and sensitive magnetic sensor for the non-destructive defect detection applications.

Relationship between negative symptoms and neurocognitive functions in adolescent and adult patients with first-episode schizophrenia.

BACKGROUND: This study aimed to explore differences in links between negative symptoms and neurocognitive deficits in adolescent and adult patients with first-episode schizophrenia. Schizophrenia is a mental disorder often characterized by positive and negative symptoms, reduced emotional expression, excitatory status, and poor cognitive ability. The severity of negative symptoms in patients with schizophrenia was reported to be more related to poor quality of life, weak functional ability, and heavy burden from families than with the severity of positive symptoms. Previous studies suggested correlations between the severity of negative symptoms in patients with schizophrenia and neurocognitive deficits. METHODS: This study included 92 patients (33 adolescents and 59 adults) with first-episode schizophrenia and 57 healthy people matched by age and education level. Neurocognitive functions and clinical symptoms were assessed using a standardized questionnaire. RESULTS: Patients with first-episode schizophrenia showed neurocognitive deficits in most neuropsychological assessments compared with healthy people. With the variable of education level controlled, the negative factor score of adolescent patients with first-episode schizophrenia was strongly correlated with more time spent in part 1 (r = .646) and part 2 (r = .663) of the trail making test, and moderately correlated to more perseverative errors (r = .425) of the Wisconsin card sorting test and fewer correct trials 2 (r = -.425) of the continuous performance test. However, no such correlations were found in adult patients. CONCLUSIONS: This study indicated significant correlations between negative symptoms and most neurocognitive functions in patients with first-episode schizophrenia, with a stronger correlation in adolescent patients. TRIAL REGISTRATION: The trial registration number is ChiCTR-COC-14005302 , while retrospectively registered on January 5, 2014.

Machine learning-enhanced predictive modeling for arbitrary deterministic lateral displacement design and test.

The separation of biological particles like cells and macromolecules from liquid samples is vital in clinical medicine, supporting liquid biopsies and diagnostics. Deterministic Lateral Displacement (DLD) is prominent for sorting particles in microfluidics by size. However, the design, fabrication, and testing of DLDs are complex and time-consuming. Researchers typically rely on finite element analysis to predict particle trajectories, which are crucial in evaluating the performance of DLD. Traditional particle trajectory predictions through finite element analysis often inaccurately reflect experimental results due to manufacturing and experimental variabilities. To address this issue, we introduced a machine learning-enhanced approach, combining past experimental data and advanced modeling techniques. Our method, using a dataset of 132 experiments from 40 DLD chips and integrating finite element simulation with a microfluidic-optimized particle simulation algorithm (MOPSA) and a Random Forest model, improves trajectory prediction and critical size determination without physical tests. This enhanced accuracy in simulation across various DLD chips speeds up development. Our model, validated against three DLD chip designs, showed a high correlation between predicted and experimental particle trajectories, streamlining chip development for clinical applications.

Rational Design of a Surface Acoustic Wave Device for Wearable Body Temperature Monitoring.

Continuous monitoring of vital signs based on advanced sensing technologies has attracted extensive attention due to the ravages of COVID-19. A maintenance-free and low-cost passive wireless sensing system based on surface acoustic wave (SAW) device can be used to continuously monitor temperature. However, the current SAW-based passive sensing system is mostly designed at a low frequency around 433 MHz, which leads to the relatively large size of SAW devices and antenna, hindering their application in wearable devices. In this paper, SAW devices with a resonant frequency distributed in the 870 MHz to 960 MHz range are rationally designed and fabricated. Based on the finite-element method (FEM) and coupling-of-modes (COM) model, the device parameters, including interdigital transducer (IDT) pairs, aperture size, and reflector pairs, are systematically optimized, and the theoretical and experimental results show high consistency. Finally, SAW temperature sensors with a quality factor greater than 2200 are obtained for real-time temperature monitoring ranging from 20 to 50 °C. Benefitting from the higher operating frequency, the size of the sensing system can be reduced for human body temperature monitoring, showing its potential to be used as a wearable monitoring device in the future.

Analytical and experimental study of a valveless piezoelectric micropump with high flowrate and pressure load.

Miniaturized gas pumps based on electromagnetic effect have been intensively studied and widely applied in industries. However, the electromagnetic effect-based gas pumps normally have large sizes, high levels of noises and high power consumption, thus they are not suitable for wearable/portable applications. Herein, we propose a high-flowrate and high-pressure load valveless piezoelectric micropump with dimensions of 16 mm*16 mm*5 mm. The working frequency, vibration mode and displacement of the piezoelectric actuator, the velocity of gas flow, and the volume flowrate of the micropump are analyzed using the finite element analysis method. The maximum vibration amplitude of the piezoelectric actuator reaches ~29.4 µm. The output gas flowrate of the pump is approximately 135 mL/min, and the maximum output pressure exceeds 40 kPa. Then, a prototype of the piezoelectric micropump is fabricated. Results show that performance of the micropump is highly consistent with the numerical analysis with a high flowrate and pressure load, demonstrated its great potential for wearable/portable applications, especially for blood pressure monitoring.

Machine Learning Assisted Wearable Wireless Device for Sleep Apnea Syndrome Diagnosis.

Sleep apnea syndrome (SAS) is a common but underdiagnosed health problem related to impaired quality of life and increased cardiovascular risk. In order to solve the problem of complicated and expensive operation procedures for clinical diagnosis of sleep apnea, here we propose a small and low-cost wearable apnea diagnostic system. The system uses a photoplethysmography (PPG) optical sensor to collect human pulse wave signals and blood oxygen saturation synchronously. Then multiscale entropy and random forest algorithms are used to process the PPG signal for analysis and diagnosis of sleep apnea. The SAS determination is based on the comprehensive diagnosis of the PPG signal and blood oxygen saturation signal, and the blood oxygen is used to exclude the error induced by non-pathological factors. The performance of the system is compared with the Compumedics Grael PSG (Polysomnography) sleep monitoring system. This simple diagnostic system provides a feasible technical solution for portable and low-cost screening and diagnosis of SAS patients with a high accuracy of over 85%.

Aberrant single-subject morphological brain networks in first-episode, treatment-naive adolescents with major depressive disorder.

Background: Neuroimaging-based connectome studies have indicated that major depressive disorder (MDD) is associated with disrupted topological organization of large-scale brain networks. However, the disruptions and their clinical and cognitive relevance are not well established for morphological brain networks in adolescent MDD. Objective: To investigate the topological alterations of single-subject morphological brain networks in adolescent MDD. Methods: Twenty-five first-episode, treatment-naive adolescents with MDD and 19 healthy controls (HCs) underwent T1-weighted magnetic resonance imaging and a battery of neuropsychological tests. Single-subject morphological brain networks were constructed separately based on cortical thickness, fractal dimension, gyrification index, and sulcus depth, and topologically characterized by graph-based approaches. Between-group differences were inferred by permutation testing. For significant alterations, partial correlations were used to examine their associations with clinical and neuropsychological variables in the patients. Finally, a support vector machine was used to classify the patients from controls. Results: Compared with the HCs, the patients exhibited topological alterations only in cortical thickness-based networks characterized by higher nodal centralities in parietal (left primary sensory cortex) but lower nodal centralities in temporal (left parabelt complex, right perirhinal ectorhinal cortex, right area PHT and right ventral visual complex) regions. Moreover, decreased nodal centralities of some temporal regions were correlated with cognitive dysfunction and clinical characteristics of the patients. These results were largely reproducible for binary and weighted network analyses. Finally, topological properties of the cortical thickness-based networks were able to distinguish the MDD adolescents from HCs with 87.6% accuracy. Conclusion: Adolescent MDD is associated with disrupted topological organization of morphological brain networks, and the disruptions provide potential biomarkers for diagnosing and monitoring the disease.

Electric-Field-Resonance-Based Wireless Triboelectric Nanogenerators and Sensors.

Energy harvesting and energy transmission are the key technologies for self-powered systems; thus, the combination of these two is urgently needed. An innovative electric field resonance (EFR)-based wireless triboelectric nanogenerator (TENG) is proposed herein. By integrating the TENG with a capacitive coupler, the output of the TENG can be transmitted wirelessly from the transmitter to the receiver in the form of an oscillating signal with an energy-transfer efficiency of 67.8% for a 5 cm distance. Theoretical models of the EFR-TENG system are established, showing excellent agreement with the experimental results. It is demonstrated that the flexible EFR-TENG worn on the wrist can drive a digital watch wirelessly or light up at least 40 light-emitting diodes in series. The EFR-TENG is further utilized for spontaneous wireless sensing with a transmission distance up to 2.3 m with high system tolerance, showing the great potential of this novel strategy for energy harvesting and real-time wireless sensing applications.

Predicting the fluid behavior of random microfluidic mixers using convolutional neural networks.

With the various applications of microfluidics, numerical simulation is highly recommended to verify its performance and reveal potential defects before fabrication. Among all the simulation parameters and simulation tools, the velocity field and concentration profile are the key parts and are generally simulated using finite element analysis (FEA). In our previous work [Wang et al., Lab Chip, 2016, 21, 4212-4219], automated design of microfluidic mixers by pre-generating a random library with the FEA was proposed. However, the duration of the simulation process is time-consuming, while the matching consistency between limited pre-generated designs and user desire is not stable. To address these issues, we inventively transformed the fluid mechanics problem into an image recognition problem and presented a convolutional neural network (CNN)-based technique to predict the fluid behavior of random microfluidic mixers. The pre-generated 10 513 candidate designs in the random library were used in the training process of the CNN, and then 30 757 brand new microfluidic mixer designs were randomly generated, whose performance was predicted by the CNN. Experimental results showed that the CNN method could complete all the predictions in just 10 seconds, which was around 51 600× faster than the previous FEA method. The CNN library was extended to contain 41 270 candidate designs, which has filled up those empty spaces in the fluid velocity versus solute concentration map of the random library, and able to provide more choices and possibilities for user desire. Besides, the quantitative analysis has confirmed the increased compatibility of the CNN library with user desire. In summary, our CNN method not only presents a much faster way of generating a more complete library with candidate mixer designs but also provides a solution for predicting fluid behavior using a machine learning technique.

Use of longer sized screws is a salvage method for broken pedicles in osteoporotic vertebrae.

Screw loosening due to broken pedicles is a common complication resulting from the insertion of screws either with inadequate diameters or into an osteoporotic pedicle. In this novel in vitro study, we tried to clarify the contribution of the pedicle to screw fixation and subsequent salvage strategies using longer or larger-diameter screws in broken pedicles. Sixty L4 fresh-frozen lumbar vertebrae harvested from mature pigs were designed as the normal-density group (n = 30) and decalcified as the osteoporosis group (n = 30). Three modalities were randomly assigned as intact pedicle (n = 30), semi-pedicle (n = 15), and non-pedicle (n = 15) in each group. Three sizes of polyaxial screws (diameter × length of 6.0 mm × 45 mm, 6.0 mm × 50 mm, and 6.5 mm × 45 mm) over five trials were used in each modality. The associations between bone density, pedicle modality and screw pullout strength were analyzed. After decalcification for 4 weeks, the area bone mineral density decreased to approximately 56% (p < 0.05) of the normal-density group, which was assigned as the osteoporosis group. An appropriate screw trajectory and insertional depth were confirmed using X-ray imaging prior to pullout testing in both groups. The pullout forces of larger-diameter screws (6.5 mm × 45 mm) and longer screws (6.0 mm × 50 mm) were significantly higher (p < 0.05) in the semi- and non-pedicle modalities in the normal-density group, whereas only longer screws (6.0 mm × 50 mm) had a significantly higher (p < 0.05) pullout force in the non-pedicle modalities in the osteoporosis group. The pedicle plays an important role in both the normal bone density group and the osteoporosis group, as revealed by analyzing the pullout force percentage contributed by the pedicle. Use of a longer screw would be a way to salvage a broken pedicle of osteoporotic vertebra.

Disrupted intrinsic functional connectivity of the cognitive control network underlies disease severity and executive dysfunction in first-episode, treatment-naive adolescent depression.

BACKGROUND: Previous neuroimaging studies have showed that imbalanced functional integration of distributed large-scale brain networks is associated with pathophysiological characteristics of major depressive disorder (MDD). However, the association between network integrative disturbances and clinical features and cognitive functions remains largely unclear in adolescent MDD. This study investigated the neural correlates of abnormal functional connectivity networks with clinical and cognitive characteristics in adolescent MDD. METHODS: Twenty-eight first-episode, treatment-naive adolescents with MDD and 24 well-matched healthy controls (HCs) underwent functional magnetic resonance imaging (fMRI) and a battery of cognitive tests. A seed-based functional connectivity (FC) approach was used to depict connectivity patterns of the cognitive control network (CCN), affective network (AN) and default mode network (DMN), whose between-group differences were correlated with clinical variables and cognitive functions in the patients. RESULTS: Compared with the HCs, the MDD patients exhibited impaired executive functions. The FC analysis revealed lower CCN FC with the temporal, parietal and frontal regions and the limbic system, higher AN FC with the temporal and occipital regions and the cerebellum, and lower DMN FC with the cerebellum and insula. Interestingly, the decreased CCN FC was related to disease severity (with the inferior frontal gyrus) and executive dysfunctions (with the middle cingulate gyrus and supramarginal gyrus) in the patients. LIMITATIONS: The main limitations were the relatively small sample size and suboptimal imaging parameters. CONCLUSION: Functional alteration of CCN during the developmentally sensitive period may be important in the neurobiology of adolescent MDD.

Neuronavigation-Guided rTMS for the Treatment of Depressive Patients With Suicidal Ideation: A Double-Blind, Randomized, Sham-Controlled Trial.

During the last decade, the problem of suicide has become more serious in individuals with depression. Repetitive transcranial magnetic stimulation (rTMS) is an effective treatment for major depressive disorder (MDD). This study aims to investigate the efficacy of magnetic resonance imaging (MRI)-based neuronavigation-guided daily high-dose rTMS for rapidly improving suicidal ideation in treatment-naive patients with MDD. In the present 1-week double-blind study, 42 treatment-naive patients with MDD with suicidal ideation were randomly assigned to the treatment of escitalopram oxalate tablets (10 mg/d) in combination with either active (n = 21) or sham (n = 21) rTMS. The TMS coil was positioned over a specified target location (-44, 40, and 29) in left dorsolateral prefrontal cortex based on MRI data. The severity of suicidal ideation was measured by the Beck Scale for Suicide Ideation (BSI). The 24-item Hamilton Depression Rating Scale (HAMD-24) and Montgomery-Åsberg Depression Rating Scale (MADRS) were utilized to assess the severity of depression. The Wisconsin Card Sorting Test, Continuous Performance Test, and Stroop Color-Word Test were adopted to assess executive function. In contrast to the sham group, the active rTMS group showed a significantly greater BSI score reduction at the third day and the seventh day (P < 0.001). Moreover, the active rTMS group showed a significantly greater HAMD (P < 0.001) and MADRS (P < 0.001) score reduction at the seventh day in comparison to the sham group. The present findings suggested that the neuronavigation-guided high-dose rTMS may be a novel method to rapidly reduce suicidal ideation and mitigate depressive symptoms.

Conjunction of triboelectric nanogenerator with induction coils as wireless power sources and self-powered wireless sensors.

Here we demonstrate a magnetic resonance coupling based wireless triboelectric nanogenerator (TENG) and fully self-powered wireless sensors. By integrating a microswitch and an inductor with the TENG, the pulsed voltage output is converted into a sinusoidal voltage signal with a fixed frequency. This can be transmitted wirelessly from the transmit coil to the resonant-coupled receiver coil with an efficiency of 73% for a 5 cm distance between the two coils (10 cm diameter). Analytic models of the oscillating and coupled voltage signals for the wireless energy transfer are developed, showing excellent agreement with the experimental results. A TENG of 40 × 50 mm2 can wirelessly light up 70 LEDs or charge up a 15 µF capacitor to 12.5 V in ~90 s. The system is further utilized for two types of fully self-powered wireless chipless sensors with no microelectronic components. The technologies demonstrate an innovative strategy for a wireless 'green' power source and sensing.

The relationship between the alterations in metabolite levels in the dorsolateral prefrontal cortex and clinical symptoms of patients with first-episode schizophrenia: a one year follow-up study.

BACKGROUND: Reduced brain metabolites such as N-acetyl-aspartate (NAA), glutamate (Glx), Choline (Cho) and myo-inositol (MI) have been repeatedly found in first-episode schizophrenia (FES) and suggest neuronal loss or dysfunction. However, the potential relationship between the metabolite level and the clinical symptoms or the recovery of FES remained unclear. OBJECTIVES: This study aimed to investigate the correlation between the alterations in dorsolateral prefrontal cortex (DLPFC) metabolite levels of patients with first-episode schizophrenia (FES) and the changes in clinical symptoms after one year treatment. MATERIALS AND METHODS: FES patients underwent 1H-MRS scan twice: one time at the baseline and the other one year later, while the healthy group patients underwent only once at the baseline time. The symptom severity of patients was measured by PANSS. PRINCIPAL OBSERVATIONS: An increase in the NAA/Cr level was detected in the left DLPFC of patients with FES. The change in the NAA/Cr level was significantly correlated with the alteration in their PANSS-P score. The Cho/Cr levels on both sides of DLPFC in patients with FES were lower compared with the healthy controls both at the baseline and after the treatment. The NAA/Cr and MI/Cr levels in the right DLPFC were decreased after the treatment. CONCLUSIONS: (1) the depletion of NAA in left DLPFC might be a state characteristic; (2) the Cho/Cr level might be the potential endophenotype of schizophrenia; (3) the decrease of NAA/Cr and MI/Cr level in right DLPFC might be due to the development of schizophrenia.

Ultra-thin atom layer deposited alumina film enables the precise lifetime control of fully biodegradable electronic devices.

Atomic layer deposited (ALD) ultra-thin alumina film is proposed to control the operational lifetimes of fully biodegradable (FB-) surface sensitive surface acoustic wave (SAW) devices. SAW devices encapsulated with conventional thick organic materials fail to function effectively, while devices with an ultra-thin alumina encapsulation layer (AEL) function normally with high performance. After being subjected to degradation in water, a FB-SAW device with no AEL starts to degrade immediately and fails within 8 h, due to dissolution of the tungsten electrode and piezoelectric material (ZnO). The coating of an ultra-thin AEL on the surfaces prevents SAW devices from undergoing degradation in water and enables SAW devices to perform normally before the AEL is dissolved. The stable operation lifetimes of SAW devices are linearly dependent on the AEL thickness, thus allowing for the design of devices with precisely controlled operational lifetimes and degradation times. The results show that all the materials used could be degraded; also, in vitro cytotoxicity tests indicate that the encapsulated FB-SAW devices are biocompatible, and cells can adhere and proliferate on them normally, demonstrating great potential for broader biodegradable electronic device applications.