This PDF file contains the front matter associated with SPIE Proceedings Volume 13059, including the Title Page, Copyright information, Table of Contents, and Conference Committee information.

Single Photon Detection (SPD) is the essential technology for the future of quantum cytometry and quantum biology. We have been developing SPD technology previously reported at DCS2022 but recently achieved detection and recording of photoelectron (PE) pulse width ⪅500ps with 1Gcps saturation count with near 7LOG Dynamic Range (DR). The current challenge involves developing a spectral photon detection system that works in the range from ultraviolet to near infrared region. We have developed a six-decade dynamic range spectrometer from 360nm to 820nm, with a 42 channels fiber array (42CH) that distributes each spectral window onto an individual pixel-coupled silicon photomultiplier (SiPM), each channel has a 10.9nm bandwidth. The detected PE streams of the 42CH are captured with an FPGA at 10Gs/s with 100ps time resolution using multi-GHz electronics and thermoelectric cooling, and produce a huge data stream of 420Gs/s. We have identified interference problems on the system which arise from using conventional packaging with gold wire connection in dry nitrogen such as oscillation, crosstalk between adjacent channels and interference from external radiation such as Wi-Fi and cellular RF signals. To resolve electrical interference and improve signal quality, the sensor chips were mounted on an eight-layer Chip-On-Board (COB). Improving the sensor environment was the other focus for our system. We have designed a two stagesthermoelectric device targeted at -30°C with a moisture getter in the sensor package to reduce the thermal electron and the dark count of the SiPM. This design is an innovative approach in the packaging method that helps to control the environment inside the sensor. Earlier photon spectroscopy required a considerable time to scan a full spectral range using a monochromator. Our newly developed 42CH multiwavelength spectrometer allows the capture of a spectral fingerprint in microseconds to microseconds with potential readout in SI units. The system under development will contribute various applications in the fast-developing quantum field.

This paper explores the development of innovative materials for the dielectric energy storage for space components. The CaCu₃Ti₄O₁₂ or CCTO belonging to perovskite family is of interest due to its colossal dielectric constant. It was demonstrated that materials synthesized at low temperature show nonequilibrium state and exhibit differences in the dielectric and resistivity values. The goal is to obtain high dielectric constant along with high resistivity values for achieving enhanced breakdown voltage. By using other members of the perovskite structures, it was demonstrated that similar colossal dielectric constant is observed and is dependent on processing methods. We have used heterovalent and dissimilar sized atom to replace Ca⁺² ion. Accordingly, we replaced Ca⁺² ion with heavy Ga⁺³ ion and developed gallium-based material system, Ga_2/3 Cu₃Ti₄O₁₂. Following successful synthesis, we measured its dielectric constant and resistivity and compared with CCTO material system. Results of five sets of samples showed that lower temperature processing demonstrated mechanism of grain growth, but due to copper flow in high temperature processed samples dielectric constant and resistivity values were different.

Increasing water pollution poses a serious threat to both humankind and animals in the current situation. Low cost optical especially photocatalytic material is of utmost relevance to improve situation and meet the global energy demand with little environmental damage. The aim of this study is to develop low-cost low temperature reproducible method to synthesize multifunctional material suitable for degradation of a very dangerous water contaminant dye under visible light exposure. A semiwet chemical route was used to synthesize a multifunctional Bi₁₂GeO₂₀ compound suitable for photocatalytic activity for the degradation of Rhodamine B (RhB) dye under visible light exposure. Bi₁₂GeO₂₀ (BGO) ceramic with polycrystalline structure was prepared successfully e using a low temperature chemical process. X-ray powder diffraction reveals that single-phase BGO ceramic was formed. Nanosized BGO ceramic particles that had been stabilized, XRD and TEM to showed particle sizes in the 60–10 nm range. Due to the favorable band gap (2.72 eV) and the sillenite type Bi₁₂GeO₂₀ exhibits strong photocatalytic activity for the degradation of Rhodamine B (RhB) dye under visible light exposure.

Binary and ternary selenide crystals have been proven as multifunctional for optical sensors and laser applications. The aim of this study was to evaluate reactive flux growth process of the doped zinc selenide crystals and compared with bulk Physical Vapor Transport (PVT) grown large single crystals. The experimental process of synthesis involved PVP (Polyvinyl Pyrrolidone) flux dissolved in DI water which was heated at 65°C, stirred until all PVP dissolved. We added Se powder dissolved in ethanol and heated again for few minutes. We added ZnCl₂ solution in ethanol/Se mixture and heated at well below 100 ⁰C. Water and ethanol solvent was separated and placed at 200C. The residue material was doped with transition metal. This material was characterized for the luminescence and compared with the results of bulk crystals grown by PVD process.

This research plunges into the rapidly growing radar technology in the medical sector, putting emphasis on its possibility to revolutionize elderly care and health monitoring among the aging global population. Based on a systematic literature review and rigorous bibliometric analysis, we discuss radar technology application in healthcare, focusing on its potential for non-invasive, high-accuracy diagnosis and continuous patient monitoring. Our findings highlight the critical harmony between radar technology and the advances in machine learning, artificial intelligence, and data analytics, which open the door to smart healthcare solutions. These advancements will improve early disease detection, fall risk prevention, and real-time health monitoring, resulting in quick medical responses. This study endeavors to offer useful knowledge to researchers, practitioners, and policymakers who are working towards the use of technology for better health in the context of the demographic changes that the world is experiencing in terms of an ageing population by mapping the current research landscape, identifying the existing trends and gaps, and proposing the future direction of research.

Graphene, a single sheet of carbon atoms arranged in a two-dimensional (2-D) honeycomb lattice extracted from bulk three-dimensional (3-D) graphite, has shown great promise towards low-profile sensing applications. Several studies have demonstrated its potential in acquiring 2-D electrophysiological measurements of the human body including the use of electromyography (EMG). Electromyograms require a minimum of two electrodes, making them a cost-effective option for the study of 2-D conductors interfaced to the human body. Although EMG signals are typically no more than 5 mV, they can be easily visualized through amplification with a gain resistor on a prototype circuit. In this study, preliminary EMG measurements of antagonist-agonist muscle pairs are collected through utilization of commercial electrodes to yield statistically significant results on the effect of gain on the Signal-to-Noise-Ratio (SNR) and on quantitative measurements of muscle force and associated amplitude. This information is then applied towards the exploration of producing graphene electrodes for biosensing. Presently, there have been limited studies on inkjet-printed electrodes for this purpose, with methods typically favoring screen-printing techniques. Therefore, there is value in analyzing reliable fabrication methods with graphene ink towards the production of devices for strain-dependent sensing and biosensing. To do this, graphene ink was processed via liquid-phase exfoliation with a mixture of graphite powder with typical solvents and other additives. This ink was printed on an SiO2/Si substrate to form electrodes for voltage testing in addition to electrode formation on flexible substrates for dynamic strain sensing. Here the conductivity was verified through strain-dependent testing, and the flexible graphene devices demonstrated live current changes at variable bending angles and in opposite profiles which we discuss in this work.

Substance Use Disorder (SUD) represents a pervasive global health crisis characterized by the compulsive and detrimental use of psychoactive substances. In this study, we explore the functional connectivity disparities between two age- and sex-matched groups comprising 53 individuals with Cocaine Use Disorder (CUD) and 52 Healthy Control (HC) subjects. We employed resting-state fMRI data, which were preprocessed using the CONN toolbox, ensuring high-quality data for subsequent analysis. The CONN toolbox has a default atlas of 164 ROIs based on the FSL-Harvard Oxford atlas and the automated Anatomical Labeling Atlas (AAL). The investigation extended into first level and second level-analysis features within the CONN toolbox to discern functional connectivity patterns between these two groups. At the group level analysis centered on contrasting CUD patients and HCs, we particularly focused on the Region-of-Interest (ROI)-ROI connectivity maps in this study. This study revealed some key findings: Firstly, we observed that HC subjects exhibited significantly stronger connectivity between the Superior Temporal Gyrus (STG) and regions of interest within the basal ganglia network (BSL), compared to individuals with CUD. Secondly, the HC group demonstrated heightened connectivity between regions of interest belonging to the visual network and the cerebellum, contrasting with the weaker connectivity observed in the CUD group. Lastly, there was a notable increase in connectivity between the Inferior Temporal Gyrus, temporooccipital part (toITG), and the cerebellum in individuals with CUD, further emphasizing the disruption in functional connectivity within this population. Understanding these functional connectivity differences may inform future interventions and diagnostic approaches in the context of cocaine use disorder.

Substance Use Disorder (SUD) is a complex condition with profound effects on brain function. Understanding the altered functional connectivity patterns in the brains of SUD patients is crucial for unraveling the neurological underpinnings of this disorder. This study employs Energy Landscape Analysis, an energy-based machine learning technique, to investigate whole brain Regions of Interest (ROI) functional connectivity differences between SUD patients and healthy controls. The challenge with Energy Landscape Analysis lies in selecting the appropriate ROI from the extensive brain atlas. In this study, seed-based connectivity was utilized to identify relevant ROIs, overcoming the limitation of analyzing only a limited number of ROIs. The dataset comprised 53 cocaine users and 52 age- and sex-matched healthy controls, with fMRI data preprocessed using the CONN toolbox. ROI-ROI seed-based pair connectivity was derived through first and second level analyses. The identified sub-ROIs were categorized into default CONN network affiliations and bundled into Superior Temporal Gyrus (STG), Inferior Temporal Gyrus, temporooccipital part (toITG), Visual Primary (VIS-P), Auditory (AUD), Cerebellum, Basal Ganglia (BSL), and Thalamus (THL). Significance testing revealed eight connectivity states among all above regions with p-values that satisfy Bonferroni correction between controls and patients. Notably, the connectivity states with the lowest p-values revealed a distinctive pattern: STG (auditory attention) toITG were disconnected from the rest of the networks. This finding underscores the importance of investigating specific network disruptions in SUD, shedding light on potential neural mechanisms underlying the disorder. In summary, our study utilizes Energy Landscape Analysis to explore whole brain ROI functional connectivity in SUD, revealing disrupted connectivity patterns that may have implications for understanding the neural basis of this disorder. These findings may ultimately inform targeted interventions and treatment strategies for individuals with SUD.

Auditory hallucinations are a hallmark symptom of mental disorders such as schizophrenia, psychosis, and bipolar disorder. The biological basis for auditory perceptions and hallucinations, however, is not well understood. Understanding hallucinations may broadly reflect how our brains work — namely, by making predictions about stimuli and the environments that we navigate. In this work, we would like to use a recently developed language model to help the understanding of auditory hallucinations. Bio-inspired Large Language Models (LLMs) such as Bidirectional Encoder Representations from Transformers (BERT) and Generative Pre-trained Transformer (GPT) can generate next words based on previously generated words from the embedded space and their pre-trained library with or without inputs. The generative nature of neural networks in GPT (like self-attention) can be analogously associated with the neurophysiological sources of hallucinations. Functional imaging studies have revealed that the hyperactivity of the auditory cortex and the disruption between auditory and verbal network activity may underlie auditory hallucinations’ etiology. Key areas involved in auditory processing suggest that regions involved in verbal working memory and language processing are also associated with hallucinations. Auditory hallucinations reflect decreased activity in verbal working memory and language processing regions, including the superior temporal and inferior parietal regions. Parallels between auditory processing and LLM transformer architecture may help to decode brain functions on meaning assignment, contextual embedding, and hallucination mechanisms. Furthermore, an improved understanding of neurophysiological functions and brain architecture would bring us one step closer to creating human-like intelligence.

Active matter, such as Janus micromotors have been used for applications such as self-assembly, pollution mitigation, and drug delivery. Metal-Organic Framework (MOF)-based Janus micromotors have been recently explored as a method to increase the rate of decontamination for chemical warfare agents in solution due to favorable MOF-chemical interactions. To achieve active-matter decontamination, SiO2@UiO66@Ag MOF-based Janus micromotors were synthesized. In addition to decontamination, the MOF-based micromotors have favorable surface topography for maintaining a localized surface plasmon. This work explores the plasmonic capabilities of Ag@MOF Janus micromotors by systematically changing the amount of Ag, the size of the microparticle that is being used for the plasmonic sensing, and the underlying MOF structure. By changing these parameters, MOF-based micromotors may be able to be used as sensors by utilizing techniques such as Surface Enhanced Raman Spectroscopy (SERS).

Understanding biological samples is an important part of disease treatment and prevention. Current methods of biological analysis can be time-consuming and costly. Label-free Surface-Enhanced Raman Scattering (SERS) is a useful vibrational technique that incorporates plasmonic metal nanomaterial to amplify Raman signals. This technique requires little sample preparation and provides high informational chemical insights on the target. Herein, we use SERS to test and analyze biological samples of exosomes and bacteria. Each biological sample has similar biomolecular components that are difficult to differentiate or show small differences after interacting with other chemicals. Thus, herein, we show the incorporation of principal component analysis to understand differences and trends in the spectra. These studies highlight the powerful combination of SERS and machine learning for biological analysis.

In light of the profound global health impact of pandemics, the reliance on data-driven insights to understand disease outbreaks has never been more crucial. Malaria is a disease transmitted by mosquitoes that is endemic to specific regions and causes severe illness and death to millions each year. The sensitivity of mosquito vectors to environmental factors like temperature, precipitation, and humidity enables the mapping of areas at high risk of disease outbreaks through satellite remote sensing. This study proposes the development of a practical geospatial system that can provide early warning for malaria. It combines Geographic Information System (GIS) tools, Artificial Neural Networks (ANN) for efficient pattern recognition, robust on-ground environmental data (including epidemiological and vector ecology data), and the capabilities of satellite remote sensing. The study employs Vegetation Health Indices (VHI) derived from satellite-mounted Advanced Very High-Resolution Radiometers (AVHRR) on a weekly basis with a 4-km resolution to predict malaria risk in Bangladesh. While the focus is on Bangladesh due to its significant malaria threat, the technology developed can be adapted for use in other countries and against different disease threats. Implementing an early malaria warning system would be a significant asset to global public health efforts. It would enable targeted resource allocation for pandemic containment and serve as a vital decision-making tool for national security assessments and potential troop deployments in disease-prone regions.

Amid the SARS-CoV-2 pandemic, traditional virus detection methods like RT-qPCR face limitations in terms of infrastructure and processing time. This has spurred the development of agile diagnostic technologies, emphasizing non-invasive and rapid testing. Surface-Enhanced Raman Scattering (SERS) and Localized Surface Plasmon Resonance (LSPR) have emerged as promising alternatives. SERS, amplifying Raman signals through metal nanostructures, offers high sensitivity, high specificity, rapid response, qualitative and quantitative analysis enhanced by recent innovations like multiwell-array substrates. Integration with machine learning refines SERS's diagnostic capabilities, enabling rapid and accurate identification of SARS-CoV-2. LSPR, leveraging light-metal nanoparticle interactions, revolutionizes rapid viral detection, especially with the development of portable handheld devices. These devices enable real-time, on-site testing, proving crucial in managing infectious disease outbreaks. Their applications extend beyond SARS-CoV-2, holding potential for various pathogens.