Abstract Aerosol jet printing (AJP) is a direct-write additive manufacturing (AM) method, emerging as the process of choice for the fabrication of a broad spectrum of electronics, such as sensors, transistors, and optoelectronic devices. However, AJP is a highly complex process, prone to intrinsic gradual drifts. Consequently, real-time process monitoring and control in AJP is a bourgeoning need. The goal of this work is to establish an integrated, smart platform for in situ and real-time monitoring of the functional properties of AJ-printed electronics. In pursuit of this goal, the objective is to forward a multiple-input, single-output (MISO) intelligent learning model—based on sparse representation classification (SRC)—to estimate the functional properties (e.g., resistance) in situ as well as in real-time. The aim is to classify the resistance of printed electronic traces (lines) as a function of AJP process parameters and the trace morphology characteristics (e.g., line width, thickness, and cross-sectional area (CSA)). To realize this objective, line morphology is captured using a series of images, acquired: (i) in situ via an integrated high-resolution imaging system and (ii) in real-time via the AJP standard process monitor camera. Utilizing image processing algorithms developed in-house, a wide range of 2D and 3D morphology features are extracted, constituting the primary source of data for the training, validation, and testing of the SRC model. The four-point probe method (also known as Kelvin sensing) is used to measure the resistance of the deposited traces and as a result, to define a priori class labels. The results of this study exhibited that using the presented approach, the resistance (and potentially, other functional properties) of printed electronics can be estimated both in situ and in real-time with an accuracy of ≥ 90%.
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Challenges and Advances in Aerosol Jet Printing of Regenerated Silk Fibroin Solutions
Silk fibroin materials are manufactured using printing and coating techniques at resolutions 1–2 µm. However, current processes are unstable, of low printability and versatility, and of limited feature size, and often require use of additives to process, which can impact material functionality and performance. Although there exist well established material synthesis and formulation approaches for making processable solutions from silkworm cocoons, these approaches do not translate to the emerging fabrication processes, such as aerosol jet printing (AJP). Here, a new approach is introduced to formulate silk‐worm solutions for AJP and subsequently analyze the processing limits, due to defects such as overspray, pooling, and cloudiness. It is found that the degumming step is critical and can lead to defects such as gelling and pooling. Furthermore, it is found that there exists a narrow processing window (sheath rate as a function of ink rate) for AJP formulations without defects. As with other materials (such as metal inks), overspray is an issue during the fabrication process; however, it is minimized within the processing window. This work stands to open a pathway for manufacturing new and emerging biodegradable materials suitable for pharmaceuticals, food packaging, and electronics, among others.
more » « less- NSF-PAR ID:
- 10457502
- Publisher / Repository:
- Wiley Blackwell (John Wiley & Sons)
- Date Published:
- Journal Name:
- Advanced Materials Interfaces
- Volume:
- 7
- Issue:
- 12
- ISSN:
- 2196-7350
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Obeid, Iyad Selesnick (Ed.)Electroencephalography (EEG) is a popular clinical monitoring tool used for diagnosing brain-related disorders such as epilepsy [1]. As monitoring EEGs in a critical-care setting is an expensive and tedious task, there is a great interest in developing real-time EEG monitoring tools to improve patient care quality and efficiency [2]. However, clinicians require automatic seizure detection tools that provide decisions with at least 75% sensitivity and less than 1 false alarm (FA) per 24 hours [3]. Some commercial tools recently claim to reach such performance levels, including the Olympic Brainz Monitor [4] and Persyst 14 [5]. In this abstract, we describe our efforts to transform a high-performance offline seizure detection system [3] into a low latency real-time or online seizure detection system. An overview of the system is shown in Figure 1. The main difference between an online versus offline system is that an online system should always be causal and has minimum latency which is often defined by domain experts. The offline system, shown in Figure 2, uses two phases of deep learning models with postprocessing [3]. The channel-based long short term memory (LSTM) model (Phase 1 or P1) processes linear frequency cepstral coefficients (LFCC) [6] features from each EEG channel separately. We use the hypotheses generated by the P1 model and create additional features that carry information about the detected events and their confidence. The P2 model uses these additional features and the LFCC features to learn the temporal and spatial aspects of the EEG signals using a hybrid convolutional neural network (CNN) and LSTM model. Finally, Phase 3 aggregates the results from both P1 and P2 before applying a final postprocessing step. The online system implements Phase 1 by taking advantage of the Linux piping mechanism, multithreading techniques, and multi-core processors. To convert Phase 1 into an online system, we divide the system into five major modules: signal preprocessor, feature extractor, event decoder, postprocessor, and visualizer. The system reads 0.1-second frames from each EEG channel and sends them to the feature extractor and the visualizer. The feature extractor generates LFCC features in real time from the streaming EEG signal. Next, the system computes seizure and background probabilities using a channel-based LSTM model and applies a postprocessor to aggregate the detected events across channels. The system then displays the EEG signal and the decisions simultaneously using a visualization module. The online system uses C++, Python, TensorFlow, and PyQtGraph in its implementation. The online system accepts streamed EEG data sampled at 250 Hz as input. The system begins processing the EEG signal by applying a TCP montage [8]. Depending on the type of the montage, the EEG signal can have either 22 or 20 channels. To enable the online operation, we send 0.1-second (25 samples) length frames from each channel of the streamed EEG signal to the feature extractor and the visualizer. Feature extraction is performed sequentially on each channel. The signal preprocessor writes the sample frames into two streams to facilitate these modules. In the first stream, the feature extractor receives the signals using stdin. In parallel, as a second stream, the visualizer shares a user-defined file with the signal preprocessor. This user-defined file holds raw signal information as a buffer for the visualizer. The signal preprocessor writes into the file while the visualizer reads from it. Reading and writing into the same file poses a challenge. The visualizer can start reading while the signal preprocessor is writing into it. To resolve this issue, we utilize a file locking mechanism in the signal preprocessor and visualizer. Each of the processes temporarily locks the file, performs its operation, releases the lock, and tries to obtain the lock after a waiting period. The file locking mechanism ensures that only one process can access the file by prohibiting other processes from reading or writing while one process is modifying the file [9]. The feature extractor uses circular buffers to save 0.3 seconds or 75 samples from each channel for extracting 0.2-second or 50-sample long center-aligned windows. The module generates 8 absolute LFCC features where the zeroth cepstral coefficient is replaced by a temporal domain energy term. For extracting the rest of the features, three pipelines are used. The differential energy feature is calculated in a 0.9-second absolute feature window with a frame size of 0.1 seconds. The difference between the maximum and minimum temporal energy terms is calculated in this range. Then, the first derivative or the delta features are calculated using another 0.9-second window. Finally, the second derivative or delta-delta features are calculated using a 0.3-second window [6]. The differential energy for the delta-delta features is not included. In total, we extract 26 features from the raw sample windows which add 1.1 seconds of delay to the system. We used the Temple University Hospital Seizure Database (TUSZ) v1.2.1 for developing the online system [10]. The statistics for this dataset are shown in Table 1. A channel-based LSTM model was trained using the features derived from the train set using the online feature extractor module. A window-based normalization technique was applied to those features. In the offline model, we scale features by normalizing using the maximum absolute value of a channel [11] before applying a sliding window approach. Since the online system has access to a limited amount of data, we normalize based on the observed window. The model uses the feature vectors with a frame size of 1 second and a window size of 7 seconds. We evaluated the model using the offline P1 postprocessor to determine the efficacy of the delayed features and the window-based normalization technique. As shown by the results of experiments 1 and 4 in Table 2, these changes give us a comparable performance to the offline model. The online event decoder module utilizes this trained model for computing probabilities for the seizure and background classes. These posteriors are then postprocessed to remove spurious detections. The online postprocessor receives and saves 8 seconds of class posteriors in a buffer for further processing. It applies multiple heuristic filters (e.g., probability threshold) to make an overall decision by combining events across the channels. These filters evaluate the average confidence, the duration of a seizure, and the channels where the seizures were observed. The postprocessor delivers the label and confidence to the visualizer. The visualizer starts to display the signal as soon as it gets access to the signal file, as shown in Figure 1 using the “Signal File” and “Visualizer” blocks. Once the visualizer receives the label and confidence for the latest epoch from the postprocessor, it overlays the decision and color codes that epoch. The visualizer uses red for seizure with the label SEIZ and green for the background class with the label BCKG. Once the streaming finishes, the system saves three files: a signal file in which the sample frames are saved in the order they were streamed, a time segmented event (TSE) file with the overall decisions and confidences, and a hypotheses (HYP) file that saves the label and confidence for each epoch. The user can plot the signal and decisions using the signal and HYP files with only the visualizer by enabling appropriate options. For comparing the performance of different stages of development, we used the test set of TUSZ v1.2.1 database. It contains 1015 EEG records of varying duration. The any-overlap performance [12] of the overall system shown in Figure 2 is 40.29% sensitivity with 5.77 FAs per 24 hours. For comparison, the previous state-of-the-art model developed on this database performed at 30.71% sensitivity with 6.77 FAs per 24 hours [3]. The individual performances of the deep learning phases are as follows: Phase 1’s (P1) performance is 39.46% sensitivity and 11.62 FAs per 24 hours, and Phase 2 detects seizures with 41.16% sensitivity and 11.69 FAs per 24 hours. We trained an LSTM model with the delayed features and the window-based normalization technique for developing the online system. Using the offline decoder and postprocessor, the model performed at 36.23% sensitivity with 9.52 FAs per 24 hours. The trained model was then evaluated with the online modules. The current performance of the overall online system is 45.80% sensitivity with 28.14 FAs per 24 hours. Table 2 summarizes the performances of these systems. The performance of the online system deviates from the offline P1 model because the online postprocessor fails to combine the events as the seizure probability fluctuates during an event. The modules in the online system add a total of 11.1 seconds of delay for processing each second of the data, as shown in Figure 3. In practice, we also count the time for loading the model and starting the visualizer block. When we consider these facts, the system consumes 15 seconds to display the first hypothesis. The system detects seizure onsets with an average latency of 15 seconds. Implementing an automatic seizure detection model in real time is not trivial. We used a variety of techniques such as the file locking mechanism, multithreading, circular buffers, real-time event decoding, and signal-decision plotting to realize the system. A video demonstrating the system is available at: https://www.isip.piconepress.com/projects/nsf_pfi_tt/resources/videos/realtime_eeg_analysis/v2.5.1/video_2.5.1.mp4. The final conference submission will include a more detailed analysis of the online performance of each module. ACKNOWLEDGMENTS Research reported in this publication was most recently supported by the National Science Foundation Partnership for Innovation award number IIP-1827565 and the Pennsylvania Commonwealth Universal Research Enhancement Program (PA CURE). Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the official views of any of these organizations. REFERENCES [1] A. Craik, Y. He, and J. L. Contreras-Vidal, “Deep learning for electroencephalogram (EEG) classification tasks: a review,” J. Neural Eng., vol. 16, no. 3, p. 031001, 2019. https://doi.org/10.1088/1741-2552/ab0ab5. [2] A. C. Bridi, T. Q. Louro, and R. C. L. Da Silva, “Clinical Alarms in intensive care: implications of alarm fatigue for the safety of patients,” Rev. Lat. Am. Enfermagem, vol. 22, no. 6, p. 1034, 2014. https://doi.org/10.1590/0104-1169.3488.2513. [3] M. Golmohammadi, V. Shah, I. Obeid, and J. Picone, “Deep Learning Approaches for Automatic Seizure Detection from Scalp Electroencephalograms,” in Signal Processing in Medicine and Biology: Emerging Trends in Research and Applications, 1st ed., I. Obeid, I. Selesnick, and J. Picone, Eds. New York, New York, USA: Springer, 2020, pp. 233–274. https://doi.org/10.1007/978-3-030-36844-9_8. [4] “CFM Olympic Brainz Monitor.” [Online]. Available: https://newborncare.natus.com/products-services/newborn-care-products/newborn-brain-injury/cfm-olympic-brainz-monitor. [Accessed: 17-Jul-2020]. [5] M. L. Scheuer, S. B. Wilson, A. Antony, G. Ghearing, A. Urban, and A. I. Bagic, “Seizure Detection: Interreader Agreement and Detection Algorithm Assessments Using a Large Dataset,” J. Clin. Neurophysiol., 2020. https://doi.org/10.1097/WNP.0000000000000709. [6] A. Harati, M. Golmohammadi, S. Lopez, I. Obeid, and J. Picone, “Improved EEG Event Classification Using Differential Energy,” in Proceedings of the IEEE Signal Processing in Medicine and Biology Symposium, 2015, pp. 1–4. https://doi.org/10.1109/SPMB.2015.7405421. [7] V. Shah, C. Campbell, I. Obeid, and J. Picone, “Improved Spatio-Temporal Modeling in Automated Seizure Detection using Channel-Dependent Posteriors,” Neurocomputing, 2021. [8] W. Tatum, A. Husain, S. Benbadis, and P. Kaplan, Handbook of EEG Interpretation. New York City, New York, USA: Demos Medical Publishing, 2007. [9] D. P. Bovet and C. Marco, Understanding the Linux Kernel, 3rd ed. O’Reilly Media, Inc., 2005. https://www.oreilly.com/library/view/understanding-the-linux/0596005652/. [10] V. Shah et al., “The Temple University Hospital Seizure Detection Corpus,” Front. Neuroinform., vol. 12, pp. 1–6, 2018. https://doi.org/10.3389/fninf.2018.00083. [11] F. Pedregosa et al., “Scikit-learn: Machine Learning in Python,” J. Mach. Learn. Res., vol. 12, pp. 2825–2830, 2011. https://dl.acm.org/doi/10.5555/1953048.2078195. [12] J. Gotman, D. Flanagan, J. Zhang, and B. Rosenblatt, “Automatic seizure detection in the newborn: Methods and initial evaluation,” Electroencephalogr. Clin. Neurophysiol., vol. 103, no. 3, pp. 356–362, 1997. https://doi.org/10.1016/S0013-4694(97)00003-9.more » « less
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Conventional lithium-ion batteries are unable to meet the increasing demands for high-energy storage systems, because of their limited theoretical capacity. 1 In recent years, intensive attention has been paid to enhancing battery energy storage capability to satisfy the increasing energy demand in modern society and reduce the average energy capacity cost. Among the candidates for next generation high energy storage systems, the lithium sulfur battery is especially attractive because of its high theoretical specific energy (around 2600 W h kg-1) and potential cost reduction. In addition, sulfur is a cost effective and environmentally friendly material due to its abundance and low-toxicity. 2 Despite all of these advantages, the practical application of lithium sulfur batteries to date has been hindered by a series of obstacles, including low active material loading, poor cycle life, and sluggish sulfur conversion kinetics. 3 Achieving high mass loading cathode in the traditional 2D planar thick electrode has been challenged. The high distorsion of the traditional planar thick electrodes for ion/electron transfer leads to the limited utilization of active materials and high resistance, which eventually results in restricted energy density and accelerated electrode failure. 4 Furthermore, of the electrolyte to pores in the cathode and utilization ratio of active materials. Catalysts such as MnO 2 and Co dopants were employed to accelerate the sulfur conversion reaction during the charge and discharge process. 5 However, catalysts based on transition metals suffer from poor electronic conductivity. Other catalysts such as transition metal dopants are also limited due to the increased process complexities. . In addition, the severe shuttle effects in Li-S batteries may lead to fast failures of the battery. Constructing a protection layer on the separator for limiting the transmission of soluble polysulfides is considered an effective way to eliminate the shuttle phenomenon. However, the soluble sulfides still can largely dissolve around the cathode side causing the sluggish reaction condition for sulfur conversion. 5 To mitigate the issues above, herein we demonstrate a novel sulfur electrode design strategy enabled by additive manufacturing and oxidative vapor deposition (oCVD). Specifically, the electrode is strategically designed into a hierarchal hollow structure via stereolithography technique to increase sulfur usage. The active material concentration loaded to the battery cathode is controlled precisely during 3D printing by adjusting the number of printed layers. Owing to its freedom in geometry and structure, the suggested design is expected to improve the Li ions and electron transport rate considerably, and hence, the battery power density. The printed cathode is sintered at 700 °C at N 2 atmosphere to achieve carbonization of the cathode during which intrinsic carbon defects (e.g., pentagon carbon) as catalytic defect sites are in-situ generated on the cathode. The intrinsic carbon defects equipped with adequate electronic conductivity. The sintered 3D cathode is then transferred to the oCVD chamber for depositing a thin PEDOT layer as a protection layer to restrict dissolutions of sulfur compounds in the cathode. Density functional theory calculation reveals the electronic state variance between the structures with and without defects, the structure with defects demonstrates the higher kinetic condition for sulfur conversion. To further identify the favorable reaction dynamic process, the in-situ XRD is used to characterize the transformation between soluble and insoluble polysulfides, which is the main barrier in the charge and discharge process of Li-S batteries. The results show the oCVD coated 3D printed sulfur cathode exhibits a much higher kinetic process for sulfur conversion, which benefits from the highly tailored hierarchal hollow structure and the defects engineering on the cathode. Further, the oCVD coated 3D printed sulfur cathode also demonstrates higher stability during long cycling enabled by the oCVD PEDOT protection layer, which is verified by an absorption energy calculation of polysulfides at PEDOT. Such modeling and analysis help to elucidate the fundamental mechanisms that govern cathode performance and degradation in Li-S batteries. The current study also provides design strategies for the sulfur cathode as well as selection approaches to novel battery systems. References: Bhargav, A., (2020). Lithium-Sulfur Batteries: Attaining the Critical Metrics. Joule 4 , 285-291. Chung, S.-H., (2018). Progress on the Critical Parameters for Lithium–Sulfur Batteries to be Practically Viable. Advanced Functional Materials 28 , 1801188. Peng, H.-J.,(2017). Review on High-Loading and High-Energy Lithium–Sulfur Batteries. Advanced Energy Materials 7 , 1700260. Chu, T., (2021). 3D printing‐enabled advanced electrode architecture design. Carbon Energy 3 , 424-439. Shi, Z., (2021). Defect Engineering for Expediting Li–S Chemistry: Strategies, Mechanisms, and Perspectives. Advanced Energy Materials 11 . Figure 1more » « less
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Neurotransmitters are small molecules involved in neuronal signaling and can also serve as stress biomarkers.1Their abnormal levels have been also proposed to be indicative of several neurological diseases such as Alzheimer’s disease, Parkinson’s disease, Huntington disease, among others. Hence, measuring their levels is highly important for early diagnosis, therapy, and disease prognosis. In this work, we investigate facile functionalization methods to tune and enhance sensitivity of printed graphene sensors to neurotransmitters. Sensors based on direct laser scribing and screen-printed graphene ink are studied. These printing methods offer ease of prototyping and scalable fabrication at low cost.
The effect of functionalization of laser induced graphene (LIG) by electrodeposition and solution-based deposition of TMDs (molybdenum disulfide2and tungsten disulfide) and metal nanoparticles is studied. For different processing methods, electrochemical characteristics (such as electrochemically active surface area: ECSA and heterogenous electron transfer rate: k0) are extracted and correlated to surface chemistry and defect density obtained respectively using X-ray photoelectron spectroscopy (XPS) and Raman spectroscopy. These functionalization methods are observed to directly impact the sensitivity and limit of detection (LOD) of the graphene sensors for the studied neurotransmitters. For example, as compared to bare LIG, it is observed that electrodeposition of MoS2on LIG improves ECSA by 3 times and k0by 1.5 times.3Electrodeposition of MoS2also significantly reduces LOD of serotonin and dopamine in saliva, enabling detection of their physiologically relevant concentrations (in pM-nM range). In addition, chemical treatment of LIG sensors is carried out in the form of acetic acid treatment. Acetic acid treatment has been shown previously to improve C-C bonds improving the conductivity of LIG sensors.4In our work, in particular, acetic acid treatment leads to larger improvement of LOD of norepinephrine compared to MoS2electrodeposition.
In addition, we investigate the effect of plasma treatment to tune the sensor response by modifying the defect density and chemistry. For example, we find that oxygen plasma treatment of screen-printed graphene ink greatly improves LOD of norepinephrine up to three orders of magnitude, which may be attributed to the increased defects and oxygen functional groups on the surface as evident by XPS measurements. Defects are known to play a key role in enhancing the sensitivity of 2D materials to surface interactions, and have been explored in tuning/enhancing the sensor sensitivity.5Building on our previous work,3we apply a custom machine learning-based data processing method to further improve that sensitivity and LOD, and also to automatically benchmark different molecule-material pairs.
Future work includes expanding the plasma chemistry and conditions, studying the effect of precursor mixture in laser-induced solution-based functionalization, and understanding the interplay between molecule-material system. Work is also underway to improve the machine learning model by using nonlinear learning models such as neural networks to improve the sensor sensitivity, selectivity, and robustness.
References A. J. Steckl, P. Ray, (2018), doi:10.1021/acssensors.8b00726.
Y. Lei, D. Butler, M. C. Lucking, F. Zhang, T. Xia, K. Fujisawa, T. Granzier-Nakajima, R. Cruz-Silva, M. Endo, H. Terrones, M. Terrones, A. Ebrahimi,
Sci. Adv. 6 , 4250–4257 (2020).V. Kammarchedu, D. Butler, A. Ebrahimi,
Anal. Chim. Acta .1232 , 340447 (2022).H. Yoon, J. Nah, H. Kim, S. Ko, M. Sharifuzzaman, S. C. Barman, X. Xuan, J. Kim, J. Y. Park,
Sensors Actuators B Chem. 311 , 127866 (2020).T. Wu, A. Alharbi, R. Kiani, D. Shahrjerdi,
Adv. Mater. 31 , 1–12 (2019). -
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Abstract Animal silks, consisting of pure protein components, offer an extraordinary combination of strength, elongation, and toughness, exceeding most engineered materials. The secret to this success is their unique nanoarchitectures formed through the hierarchical self‐assembly of silk proteins. This natural process contrasts the production of artificial silk materials, which usually are directly constructed as bulk structures from silk fibroin (SF) molecules. A variety of fabrication strategies to control nanostructures of silks or to create functional materials from silk nanoscale building blocks have been developed in the recent years. These emerging fabrication strategies offer an opportunity to tailor the structure of SF at the nanoscale and provide a promising route to produce structurally and functionally optimized silk nanomaterials. Herein, the critical roles of silk nanoarchitectures in property and function of natural silk fibers are reviewed and the strategies of utilization of these silk nanobuilding blocks are outlined. Further, the state‐of‐the‐art techniques to create silk nanoarchitectures and to generate silk‐based nanocomponents are summarized. An effective approach to constructing sophisticated silk functional nanocomposites with promising applications in regenerative medicine, drug delivery, and optical and electronic device designs is provided. Further, such insights suggest templates to consider for other material systems.
