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1. Lithium niobate photonics: Unlocking the electromagnetic spectrum

   https://doi.org/10.1126/science.abj4396  

   Boes, Andreas ; Chang, Lin ; Langrock, Carsten ; Yu, Mengjie ; Zhang, Mian ; Lin, Qiang ; Lončar, Marko ; Fejer, Martin ; Bowers, John ; Mitchell, Arnan ( January 2023 , Science)

   BACKGROUND Electromagnetic (EM) waves underpin modern society in profound ways. They are used to carry information, enabling broadcast radio and television, mobile telecommunications, and ubiquitous access to data networks through Wi-Fi and form the backbone of our modern broadband internet through optical fibers. In fundamental physics, EM waves serve as an invaluable tool to probe objects from cosmic to atomic scales. For example, the Laser Interferometer Gravitational-Wave Observatory and atomic clocks, which are some of the most precise human-made instruments in the world, rely on EM waves to reach unprecedented accuracies. This has motivated decades of research to develop coherent EM sources over broad spectral ranges with impressive results: Frequencies in the range of tens of gigahertz (radio and microwave regimes) can readily be generated by electronic oscillators. Resonant tunneling diodes enable the generation of millimeter (mm) and terahertz (THz) waves, which span from tens of gigahertz to a few terahertz. At even higher frequencies, up to the petahertz level, which are usually defined as optical frequencies, coherent waves can be generated by solid-state and gas lasers. However, these approaches often suffer from narrow spectral bandwidths, because they usually rely on well-defined energy states of specific materials, which results in a rather limited spectral coverage. To overcome this limitation, nonlinear frequency-mixing strategies have been developed. These approaches shift the complexity from the EM source to nonresonant-based material effects. Particularly in the optical regime, a wealth of materials exist that support effects that are suitable for frequency mixing. Over the past two decades, the idea of manipulating these materials to form guiding structures (waveguides) has provided improvements in efficiency, miniaturization, and production scale and cost and has been widely implemented for diverse applications. ADVANCES Lithium niobate, a crystal that was first grown in 1949, is a particularly attractive photonic material for frequency mixing because of its favorable material properties. Bulk lithium niobate crystals and weakly confining waveguides have been used for decades for accessing different parts of the EM spectrum, from gigahertz to petahertz frequencies. Now, this material is experiencing renewed interest owing to the commercial availability of thin-film lithium niobate (TFLN). This integrated photonic material platform enables tight mode confinement, which results in frequency-mixing efficiency improvements by orders of magnitude while at the same time offering additional degrees of freedom for engineering the optical properties by using approaches such as dispersion engineering. Importantly, the large refractive index contrast of TFLN enables, for the first time, the realization of lithium niobate–based photonic integrated circuits on a wafer scale. OUTLOOK The broad spectral coverage, ultralow power requirements, and flexibilities of lithium niobate photonics in EM wave generation provides a large toolset to explore new device functionalities. Furthermore, the adoption of lithium niobate–integrated photonics in foundries is a promising approach to miniaturize essential bench-top optical systems using wafer scale production. Heterogeneous integration of active materials with lithium niobate has the potential to create integrated photonic circuits with rich functionalities. Applications such as high-speed communications, scalable quantum computing, artificial intelligence and neuromorphic computing, and compact optical clocks for satellites and precision sensing are expected to particularly benefit from these advances and provide a wealth of opportunities for commercial exploration. Also, bulk crystals and weakly confining waveguides in lithium niobate are expected to keep playing a crucial role in the near future because of their advantages in high-power and loss-sensitive quantum optics applications. As such, lithium niobate photonics holds great promise for unlocking the EM spectrum and reshaping information technologies for our society in the future. Lithium niobate spectral coverage. The EM spectral range and processes for generating EM frequencies when using lithium niobate (LN) for frequency mixing. AO, acousto-optic; AOM, acousto-optic modulation; χ (2) , second-order nonlinearity; χ (3) , third-order nonlinearity; EO, electro-optic; EOM, electro-optic modulation; HHG, high-harmonic generation; IR, infrared; OFC, optical frequency comb; OPO, optical paramedic oscillator; OR, optical rectification; SCG, supercontinuum generation; SHG, second-harmonic generation; UV, ultraviolet. 

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2. Sn-modified BaTiO3 thin film with enhanced polarization

   https://doi.org/10.1116/6.0002208  

   Nunn, William ; Kumar, Abinash ; Zu, Rui ; Nebgen, Bailey ; Yu, Shukai ; Kamath Manjeshwar, Anusha ; Gopalan, Venkatraman ; LeBeau, James M. ; James, Richard D. ; Jalan, Bharat ( January 2023 , Journal of Vacuum Science & Technology A)

   Hybrid molecular beam epitaxy (MBE) growth of Sn-modified BaTiO3 films was realized with varying domain structures and crystal symmetries across the entire composition space. Macroscopic and microscopic structures and the crystal symmetry of these thin films were determined using a combination of optical second harmonic generation (SHG) polarimetry and scanning transmission electron microscopy (STEM). SHG polarimetry revealed a variation in the global crystal symmetry of the films from tetragonal (P4mm) to cubic (Pm3¯m) across the composition range, x = 0 to 1 in BaTi1−xSnxO3 (BTSO). STEM imaging shows that the long-range polar order observed when the Sn content is low (x = 0.09) transformed to a short-range polar order as the Sn content increased (x = 0.48). Consistent with atomic displacement measurements from STEM, the largest polarization was obtained at the lowest Sn content of x = 0.09 in Sn-modified BaTiO3 as determined by SHG. These results agree with recent bulk ceramic reports and further identify this material system as a potential replacement for Pb-containing relaxor-based thin film devices.

    

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3. Combined noninvasive metabolic and spindle imaging as potential tools for embryo and oocyte assessment

   https://doi.org/10.1093/humrep/dez210  

   Sanchez, Tim ; Venturas, Marta ; Aghvami, S. Ali ; Yang, Xingbo ; Fraden, Seth ; Sakkas, Denny ; Needleman, Daniel J. ( December 2019 , Human Reproduction)

   Is the combined use of fluorescence lifetime imaging microscopy (FLIM)-based metabolic imaging and second harmonic generation (SHG) spindle imaging a feasible and safe approach for noninvasive embryo assessment?

   Metabolic imaging can sensitively detect meaningful metabolic changes in embryos, SHG produces high-quality images of spindles and the methods do not significantly impair embryo viability.

   Proper metabolism is essential for embryo viability. Metabolic imaging is a well-tested method for measuring metabolism of cells and tissues, but it is unclear if it is sensitive enough and safe enough for use in embryo assessment.

   This study consisted of time-course experiments and control versus treatment experiments. We monitored the metabolism of 25 mouse oocytes with a noninvasive metabolic imaging system while exposing them to oxamate (cytoplasmic lactate dehydrogenase inhibitor) and rotenone (mitochondrial oxidative phosphorylation inhibitor) in series. Mouse embryos (n = 39) were measured every 2 h from the one-cell stage to blastocyst in order to characterize metabolic changes occurring during pre-implantation development. To assess the safety of FLIM illumination, n = 144 illuminated embryos were implanted into n = 12 mice, and n = 108 nonilluminated embryos were implanted into n = 9 mice.

   Experiments were performed in mouse embryos and oocytes. Samples were monitored with noninvasive, FLIM-based metabolic imaging of nicotinamide adenine dinucleotide (NADH) and flavin adenine dinucleotide (FAD) autofluorescence. Between NADH cytoplasm, NADH mitochondria and FAD mitochondria, a single metabolic measurement produces up to 12 quantitative parameters for characterizing the metabolic state of an embryo. For safety experiments, live birth rates and pup weights (mean ± SEM) were used as endpoints. For all test conditions, the level of significance was set at P < 0.05.

   Measured FLIM parameters were highly sensitive to metabolic changes due to both metabolic perturbations and embryo development. For oocytes, metabolic parameter values were compared before and after exposure to oxamate and rotenone. The metabolic measurements provided a basis for complete separation of the data sets. For embryos, metabolic parameter values were compared between the first division and morula stages, morula and blastocyst and first division and blastocyst. The metabolic measurements again completely separated the data sets. Exposure of embryos to excessive illumination dosages (24 measurements) had no significant effect on live birth rate (5.1 ± 0.94 pups/mouse for illuminated group; 5.7 ± 1.74 pups/mouse for control group) or pup weights (1.88 ± 0.10 g for illuminated group; 1.89 ± 0.11 g for control group).

   The study was performed using a mouse model, so conclusions concerning sensitivity and safety may not generalize to human embryos. A limitation of the live birth data is also that although cages were routinely monitored, we could not preclude that some runt pups may have been eaten.

   Promising proof-of-concept results demonstrate that FLIM with SHG provide detailed biological information that may be valuable for the assessment of embryo and oocyte quality. Live birth experiments support the method’s safety, arguing for further studies of the clinical utility of these techniques.

   Supported by the Blavatnik Biomedical Accelerator Grant at Harvard University and by the Harvard Catalyst/The Harvard Clinical and Translational Science Center (National Institutes of Health Award UL1 TR001102), by NSF grants DMR-0820484 and PFI-TT-1827309 and by NIH grant R01HD092550-01. T.S. was supported by a National Science Foundation Postdoctoral Research Fellowship in Biology grant (1308878). S.F. and S.A. were supported by NSF MRSEC DMR-1420382. Becker and Hickl GmbH sponsored the research with the loaning of equipment for FLIM. T.S. and D.N. are cofounders and shareholders of LuminOva, Inc., and co-hold patents (US20150346100A1 and US20170039415A1) for metabolic imaging methods. D.S. is on the scientific advisory board for Cooper Surgical and has stock options with LuminOva, Inc.

    

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4. A Deep Learning-Based Real-time Seizure Detection System

   https://doi.org/10.1109/SPMB50085.2020.9353623  

   Shawki, N. ; Elseify, T. ; Cap, T. ; Shah, V. ; Obeid, I. ; Picone, J. ( December 2020 , Proceedings of the IEEE Signal Processing in Medicine and Biology Symposium (SPMB))

   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. 

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5. (Digital Presentation) Oxide Memristors Based on SiO 2 with Cu/Ag Alloy Metallization for Neuromorphic Computing

   https://doi.org/10.1149/MA2022-0215808mtgabs  

   Qin, Fei ; Song, Han Wook ; Qin, Fei ( October 2022 , ECS Meeting Abstracts)

   By mimicking biomimetic synaptic processes, the success of artificial intelligence (AI) has been astounding with various applications such as driving automation, big data analysis, and natural-language processing.[1-4] Due to a large quantity of data transmission between the separated memory unit and the logic unit, the classical computing system with von Neumann architecture consumes excessive energy and has a significant processing delay.[5] Furthermore, the speed difference between the two units also causes extra delay, which is referred to as the memory wall.[6, 7] To keep pace with the rapid growth of AI applications, enhanced hardware systems that particularly feature an energy-efficient and high-speed hardware system need to be secured. The novel neuromorphic computing system, an in-memory architecture with low power consumption, has been suggested as an alternative to the conventional system. Memristors with analog-type resistive switching behavior are a promising candidate for implementing the neuromorphic computing system since the devices can modulate the conductance with cycles that act as synaptic weights to process input signals and store information.[8, 9]

   The memristor has sparked tremendous interest due to its simple two-terminal structure, including top electrode (TE), bottom electrode (BE), and an intermediate resistive switching (RS) layer. Many oxide materials, including HfO₂, Ta₂O₅, and IGZO, have extensively been studied as an RS layer of memristors. Silicon dioxide (SiO₂) features 3D structural conformity with the conventional CMOS technology and high wafer-scale homogeneity, which has benefited modern microelectronic devices as dielectric and/or passivation layers. Therefore, the use of SiO₂as a memristor RS layer for neuromorphic computing is expected to be compatible with current Si technology with minimal processing and material-related complexities.

   In this work, we proposed SiO₂-based memristor and investigated switching behaviors metallized with different reduction potentials by applying pure Cu and Ag, and their alloys with varied ratios. Heavily doped p-type silicon was chosen as BE in order to exclude any effects of the BE ions on the memristor performance. We previously reported that the selection of TE is crucial for achieving a high memory window and stable switching performance. According to the study which compares the roles of Cu (switching stabilizer) and Ag (large switching window performer) TEs for oxide memristors, we have selected the TE materials and their alloys to engineer the SiO₂-based memristor characteristics. The Ag TE leads to a larger memory window of the SiO₂memristor, but the device shows relatively large variation and less reliability. On the other hand, the Cu TE device presents uniform gradual switching behavior which is in line with our previous report that Cu can be served as a stabilizer, but with small on/off ratio.[9] These distinct performances with Cu and Ag metallization leads us to utilize a Cu/Ag alloy as the TE. Various compositions of Cu/Ag were examined for the optimization of the memristor TEs. With a Cu/Ag alloying TE with optimized ratio, our SiO₂based memristor demonstrates uniform switching behavior and memory window for analog switching applications. Also, it shows ideal potentiation and depression synaptic behavior under the positive/negative spikes (pulse train).

   In conclusion, the SiO₂memristors with different metallization were established. To tune the property of RS layer, the sputtering conditions of RS were varied. To investigate the influence of TE selections on switching performance of memristor, we integrated Cu, Ag and Cu/Ag alloy as TEs and compared the switch characteristics. Our encouraging results clearly demonstrate that SiO₂with Cu/Ag is a promising memristor device with synaptic switching behavior in neuromorphic computing applications.

   Acknowledgement

   This work was supported by the U.S. National Science Foundation (NSF) Award No. ECCS-1931088. S.L. and H.W.S. acknowledge the support from the Improvement of Measurement Standards and Technology for Mechanical Metrology (Grant No. 22011044) by KRISS.

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   [9] Changet al.,Nano Letters,vol. 10, no. 4, pp. 1297-1301, 2010.

   [10] Qinet al., Physica Status Solidi (RRL) - Rapid Research Letters, pssr.202200075R1, In press, 2022.

    

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