Rapid Near-Patient Impedimetric Sensing Platform for Prostate Cancer Diagnosis.

With the global escalation of concerns surrounding prostate cancer (PCa) diagnosis, reliance on the serologic prostate-specific antigen (PSA) test remains the primary approach. However, the imperative for early PCa diagnosis necessitates more effective, accurate, and rapid diagnostic point-of-care (POC) devices to enhance the result reliability and minimize disease-related complications. Among POC approaches, electrochemical biosensors, known for their amenability and miniaturization capabilities, have emerged as promising candidates. In this study, we developed an impedimetric sensing platform to detect urinary zinc (UZn) in both artificial and clinical urine samples. Our approach lies in integrating label-free impedimetric sensing and the introduction of porosity through surface modification techniques. Leveraging a cellulose acetate/reduced graphene oxide composite, our sensor's recognition layer is engineered to exhibit enhanced porosity, critical for improving the sensitivity, capture, and interaction with UZn. The sensitivity is further amplified by incorporating zincon as an external dopant, establishing highly effective recognition sites. Our sensor demonstrates a limit of detection of 7.33 ng/mL in the 0.1-1000 ng/mL dynamic range, which aligns with the reference benchmark samples from clinical biochemistry. Our sensor results are comparable with the results of inductively coupled plasma mass spectrometry (ICP-MS) where a notable correlation of 0.991 is achieved. To validate our sensor in a real-life scenario, tests were performed on human urine samples from patients being investigated for prostate cancer. Testing clinical urine samples using our sensing platform and ICP-MS produced highly comparable results. A linear correlation with R2 = 0.964 with no significant difference between two groups (p-value = 0.936) was found, thus confirming the reliability of our sensing platform.

Topological Quantum Switching Enabled Neuroelectronic Synaptic Modulators for Brain Computer Interface.

Aging and genetic-related disorders in the human brain lead to impairment of daily cognitive functions. Due to their neural synaptic complexity and the current limits of knowledge, reversing these disorders remains a substantial challenge for brain-computer interfaces (BCI). In this work, a solution is provided to potentially override aging and neurological disorder-related cognitive function loss in the human brain through the application of the authors' quantum synaptic device. To illustrate this point, a quantum topological insulator (QTI) Bi2Se2Te-based synaptic neuroelectronic device, where the electric field-induced tunable topological surface edge states and quantum switching properties make them a premier option for establishing artificial synaptic neuromodulation approaches, is designed and developed. Leveraging these unique quantum synaptic properties, the developed synaptic device provides the capability to neuromodulate distorted neural signals, leading to the reversal of age-related disorders via BCI. With the synaptic neuroelectronic characteristics of this device, excellent efficacy in treating cognitive neural dysfunctions through modulated neuromorphic stimuli is demonstrated. As a proof of concept, real-time neuromodulation of electroencephalogram (EEG) deduced distorted event-related potentials (ERP) is demonstrated by modulation of the synaptic device array.

AI-Enabled Soft Sensing Array for Simultaneous Detection of Muscle Deformation and Mechanomyography for Metaverse Somatosensory Interaction.

Motion recognition (MR)-based somatosensory interaction technology, which interprets user movements as input instructions, presents a natural approach for promoting human-computer interaction, a critical element for advancing metaverse applications. Herein, this work introduces a non-intrusive muscle-sensing wearable device, that in conjunction with machine learning, enables motion-control-based somatosensory interaction with metaverse avatars. To facilitate MR, the proposed device simultaneously detects muscle mechanical activities, including dynamic muscle shape changes and vibrational mechanomyogram signals, utilizing a flexible 16-channel pressure sensor array (weighing ≈0.38 g). Leveraging the rich information from multiple channels, a recognition accuracy of ≈96.06% is achieved by classifying ten lower-limb motions executed by ten human subjects. In addition, this work demonstrates the practical application of muscle-sensing-based somatosensory interaction, using the proposed wearable device, for enabling the real-time control of avatars in a virtual space. This study provides an alternative approach to traditional rigid inertial measurement units and electromyography-based methods for achieving accurate human motion capture, which can further broaden the applications of motion-interactive wearable devices for the coming metaverse age.

Investigating the thermo-physical properties of a new kind of graphitic carbon nitride included ternary hybrid nanofluids and the property correlations.

In this work, a simple and facile approach was employed for the preparation of the ternary hybrids comprising of titanium dioxide, zinc oxide and graphitic nitride (designated as TZG-TH) with varying compositions of the components. In the context of complex and multi-stages involved for preparation of many of the THs in the literature, the present work uses the much simpler mythology for the preparation of TH. Nanofluids (NF) were formulated in ethylene glycol: water base fluid using TZG TH as the solid particles. Scanning electron microscope of TZG TH informs that the particles are agglomerated. High resolution transmission electron microscopy image of TZG-TH reveals the presence narrowly distributed spherical particles (having the sizes in the range 40 nm-100 nm) in sheet like structure The core level X-ray photoelectron spectrum of carbon and nitrogen elements reveal the existence of sp2 -bonded C in the C[bond, double bond]N and pyridinic and graphitic nitrogen in TZG-TH. X-ray diffraction patterns of TZG TH show the existence of anatase and hexagonal phase wurtzite crystalline structure in TH. The thermo-physical properties were determined for of the THNFs in order to elucidate the influence of compositions of the component and concentration ofof TZG-TH on the thermophysical properties. The TZG TH containing larger proportions of ZnO showed the maximum of 9.11 % and 12.1 % higher increase in viscosity than the binary and base fluid, respectively. The density of TZG THs varies from 1.079 to 1.095 cp, which is closer to the base fluid. The influence of TZG TH composition on refractive index and ultrasonic velocity indicates the existence of molecular level interactions between the nanoparticles in the TH and base fluid. The ∼210 % thermal conductivity enhancement was witnessed for the TZG TH, which is significantly higher than that of ZnO mono NF (26.9%) and TiO2 mono NF (33.0%). The influence of composition and concentration of TZG- TH on molecular interaction parameters like adiabatic compressibility, intermolecular free length, free volume, internal pressure and specific acoustic impedance are reported. The TZG TH based NF showed adequate dispersion stability as inferred from dynamic light scattering and UV-visible spectroscopy results. The results on TZG TH included THNF are new to the literature and would be helpful in exploring multifunctional properties with heat transfer capabilities for applications.

Hierarchies in Visual Pathway: Functions and Inspired Artificial Vision.

The development of artificial intelligence has posed a challenge to machine vision based on conventional complementary metal-oxide semiconductor (CMOS) circuits owing to its high latency and inefficient power consumption originating from the data shuffling between memory and computation units. Gaining more insights into the function of every part of the visual pathway for visual perception can bring the capabilities of machine vision in terms of robustness and generality. Hardware acceleration of more energy-efficient and biorealistic artificial vision highly necessitates neuromorphic devices and circuits that are able to mimic the function of each part of the visual pathway. In this paper, we review the structure and function of the entire class of visual neurons from the retina to the primate visual cortex within reach (Chapter 2) are reviewed. Based on the extraction of biological principles, the recent hardware-implemented visual neurons located in different parts of the visual pathway are discussed in detail in Chapters 3 and 4. Furthermore, valuable applications of inspired artificial vision in different scenarios (Chapter 5) are provided. The functional description of the visual pathway and its inspired neuromorphic devices/circuits are expected to provide valuable insights for the design of next-generation artificial visual perception systems.

Novel 2D MXene Cobalt Ferrite (CoF@Ti3C2) Composite: A Promising Photothermal Anticancer In Vitro Study.

In search of materials with superior capability of light-to-heat (photothermal) conversion, biocompatibility, and confinement of active photothermal materials within the cells, novel magnetic MXene-based nanocomposites are found to possess all of these criteria. The CoF@Ti3C2 composite is fabricated by a simple two-step method, including an exfoliation strategy followed by sonochemical method. MXene composite has been modified with polyvinylpyrrolidone (PVP) to improve the stability in physiological conditions. The synthesized composite was characterized with multiple analytical tools. In vitro photothermal conversion efficiency of composite was determined by the time constant method and achieved η = 34.2% with an NIR 808 nm laser. In vitro, cytotoxicity studies conducted on human malignant melanoma (Ht144) and cells validated the photothermal property of the CoF@Ti3C2-PVP composite in the presence of an NIR laser (808 nm, 1.0 W cm-2), with significantly increased cytotoxicity. Calculated IC50 values were 86 µg/mL with laser, compared to 226 µg/mL without the presence of NIR laser. Microscopic results demonstrated increased apoptosis in the presence of NIR laser. Additionally, hemolysis assay confirmed biocompatibility of CoF@Ti3C2-PVP composite for intravenous applications at the IC50 concentration. The research described in this work expands the potential applications of MXene-based nanoplatforms in the biomedical field, particularly in photothermal therapy (PTT). Furthermore, the addition of cobalt ferrite serves as a magnetic nanocomposite, which eventually helps to confine therapeutic photothermal materials inside the cells, provides enhanced photothermal conversion efficiency, and creates externally controlled theranostic nanoplatforms for cancer therapy.

Eco-Friendly Cerium-Cobalt Counter-Doped Bi2Se3 Nanoparticulate Semiconductor: Synergistic Doping Effect for Enhanced Thermoelectric Generation.

Metal chalcogenides are primarily used for thermoelectric applications due to their enormous potential to convert waste heat into valuable energy. Several studies focused on single or dual aliovalent doping techniques to enhance thermoelectric properties in semiconductor materials; however, these dopants enhance one property while deteriorating others due to the interdependency of these properties or may render the host material toxic. Therefore, a strategic doping approach is vital to harness the full potential of doping to improve the efficiency of thermoelectric generation while restoring the base material eco-friendly. Here, we report a well-designed counter-doped eco-friendly nanomaterial system (~70 nm) using both isovalent (cerium) and aliovalent (cobalt) in a Bi2Se3 system for enhancing energy conversion efficiency. Substituting cerium for bismuth simultaneously enhances the Seebeck coefficient and electrical conductivity via ionized impurity minimization. The boost in the average electronegativity offered by the self-doped transitional metal cobalt leads to an improvement in the degree of delocalization of the valence electrons. Hence, the new energy state around the Fermi energy serving as electron feed to the conduction band coherently improves the density of the state of conducting electrons. The resulting high power factor and low thermal conductivity contributed to the remarkable improvement in the figure of merit (zT = 0.55) at 473 K for an optimized doping concentration of 0.01 at. %. sample, and a significant nanoparticle size reduction from 400 nm to ~70 nm, making the highly performing materials in this study (Bi2-xCexCo2x3Se3) an excellent thermoelectric generator. The results presented here are higher than several Bi2Se3-based materials already reported.

Studies on the Functional Properties of Titanium Dioxide Nanoparticles Distributed in Silyl-Alkyl Bridged Polyaniline-Based Nanofluids.

In the present work, a new kind of nanocomposite (NC)-based solid component was prepared for formulating nanofluids (NFs). The NC comprised metal oxide (titanium dioxide, TiO2) dispersed in a conducting polymer with polyaniline (PANI) and chemically linked silyl-alkyl units in it (PSA) that were designated as T-PSA NC. The NFs with ethylene glycol (EG) as a base fluid were prepared with T-PSA NCs with various compositions of TiO2 and PSA as well for various concentrations of T-PSA NCs. The scanning electron microscopic evaluation of the NC revealed that PSA deposition on TiO2 nanoparticles (NPs) decreased particle agglomeration. The PSA coating on the TiO2 NPs did not influence the crystalline structure of the TiO2 NPs, according to the X-ray diffraction patterns. The thermophysical characterization and molecular interaction features of the NFs at 303 K including a novel inorganic-organic T-PSA NC, were detailed. Furthermore, the stability of the T-PSA NC-based NFs was investigated experimentally using the zeta potential, and the particle size distribution change was analyzed using the dynamic light scattering (DLS) method. The T-PSA NCs had particle sizes that were significantly bigger than pristine PSA and pure TiO2. Most of the preparation conditions used to produce the T-PSA NCs resulted in moderately stable suspensions in EG. The results revealed that the ultrasonic velocity increased with the increase in the concentration of T-PSA NC mass % in the NFs, the refractive index and thermal conductivity increased with the increase in the concentration, and the surface tension exhibited a linear change when the ratio of mass % concentration of the T-PSA NCs increased. The combined presence of components that synergistically contribute to the electro, thermal, optical, and rheological properties is expected to attract advanced applications for NFs.

Skin-Inspired Highly Sensitive Tactile Sensors with Ultrahigh Resolution over a Broad Sensing Range.

Flexible tactile sensors with high sensitivity, a broad pressure detection range, and high resolution are highly desired for the applications of health monitoring, robots, and the human-machine interface. However, it is still challenging to realize a tactile sensor with high sensitivity and resolution over a wide detection range. Herein, to solve the abovementioned problem, we demonstrate a universal route to develop a highly sensitive tactile sensor with high resolution and a wide pressure range. The tactile sensor is composed of two layers of microstructured flexible electrodes with high modulus and conductive cotton fabric with low modulus. By optimizing the sensing films, the fabricated tactile sensor shows a high sensitivity of 8.9 × 104 kPa-1 from 2 Pa to 250 kPa because of the high structural compressibility and stress adaptation of the multilayered composite films. Meanwhile, a fast response speed of 18 ms, an ultrahigh resolution of 100 Pa over 100 kPa, and excellent durability over 20 000 loading/unloading cycles are demonstrated. Moreover, a 6 × 6 tactile sensor array is fabricated and shows promising potential application in electronic skin (e-skin). Therefore, employing multilayered composite films for tactile sensors is a novel strategy to achieve high-performance tactile perception in real-time health monitoring and artificial intelligence.

Quantum Topological Neuristors for Advanced Neuromorphic Intelligent Systems.

Neuromorphic artificial intelligence systems are the future of ultrahigh performance computing clusters to overcome complex scientific and economical challenges. Despite their importance, the advancement in quantum neuromorphic systems is slow without specific device design. To elucidate biomimicking mammalian brain synapses, a new class of quantum topological neuristors (QTN) with ultralow energy consumption (pJ) and higher switching speed (µs) is introduced. Bioinspired neural network characteristics of QTNs are the effects of edge state transport and tunable energy gap in the quantum topological insulator (QTI) materials. With augmented device and QTI material design, top notch neuromorphic behavior with effective learning-relearning-forgetting stages is demonstrated. Critically, to emulate the real-time neuromorphic efficiency, training of the QTNs is demonstrated with simple hand gesture game by interfacing them with artificial neural networks to perform decision-making operations. Strategically, the QTNs prove the possession of incomparable potential to realize next-gen neuromorphic computing for the development of intelligent machines and humanoids.

Three dimensional architected thermoelectric devices with high toughness and power conversion efficiency.

For decades, the widespread application of thermoelectric generators has been plagued by two major limitations: heat stagnation in its legs, which limits power conversion efficiency, and inherent brittleness of its constituents, which accelerates thermoelectric generator failure. While notable progress has been made to overcome these quintessential flaws, the state-of-the-art suffers from an apparent mismatch between thermoelectric performance and mechanical toughness. Here, we demonstrate an approach to potentially enhance the power conversion efficiency while suppressing the brittle failure in thermoelectric materials. By harnessing the enhanced thermal impedance induced by the cellular architecture of microlattices with the exceptional strength and ductility (>50% compressive strain) derived from partial carbonization, we fabricate three-dimensional (3D) architected thermoelectric generators that exhibit a specific energy absorption of ~30 J g-1 and power conversion efficiency of ~10%. We hope our work will improve future thermoelectric generator fabrication design through additive manufacturing with excellent thermoelectric properties and mechanical robustness.

Emerging MXene-Based Flexible Tactile Sensors for Health Monitoring and Haptic Perception.

Due to their potential applications in physiological monitoring, diagnosis, human prosthetics, haptic perception, and human-machine interaction, flexible tactile sensors have attracted wide research interest in recent years. Thanks to the advances in material engineering, high performance flexible tactile sensors have been obtained. Among the representative pressure sensing materials, 2D layered nanomaterials have many properties that are superior to those of bulk nanomaterials and are more suitable for high performance flexible sensors. As a class of 2D inorganic compounds in materials science, MXene has excellent electrical, mechanical, and biological compatibility. MXene-based composites have proven to be promising candidates for flexible tactile sensors due to their excellent stretchability and metallic conductivity. Therefore, great efforts have been devoted to the development of MXene-based composites for flexible sensor applications. In this paper, the controllable preparation and characterization of MXene are introduced. Then, the recent progresses on fabrication strategies, operating mechanisms, and device performance of MXene composite-based flexible tactile sensors, including flexible piezoresistive sensors, capacitive sensors, piezoelectric sensors, triboelectric sensors are reviewed. After that, the applications of MXene material-based flexible electronics in human motion monitoring, healthcare, prosthetics, and artificial intelligence are discussed. Finally, the challenges and perspectives for MXene-based tactile sensors are summarized.

Electrical Contacts With 2D Materials: Current Developments and Future Prospects.

Current electrical contact models are occasionally insufficient at the nanoscale owing to the wide variations in outcomes between 2D mono and multi-layered and bulk materials that result from their distinctive electrostatics and geometries. Contrarily, devices based on 2D semiconductors present a significant challenge due to the requirement for electrical contact with resistances close to the quantum limit. The next generation of low-power devices is already hindered by the lack of high-quality and low-contact-resistance contacts on 2D materials. The physics and materials science of electrical contact resistance in 2D materials-based nanoelectronics, interface configurations, charge injection mechanisms, and numerical modeling of electrical contacts, as well as the most pressing issues that need to be resolved in the field of research and development, will all be covered in this review.

Adhesive, multifunctional, and wearable electronics based on MXene-coated textile for personal heating systems, electromagnetic interference shielding, and pressure sensing.

Adhesion between flexible devices and skin surface facilitates portability of devices and reliable signal acquisition from human body, which is essential for medical therapy devices or monitoring systems. Here, we utilize a simple, cost-effective, and scalable layer-by-layer dip-coating method to fabricate a skin-adhesive multifunctional textile-based device, consisting of three parts: low-cost and easily available airlaid paper (AP) substrate, conductive MXene sensitive layer, and adhesive polydimethylsiloxane (PDMS). The adhesive layer of lightly cross-linked PDMS enables the device to form conformal contact with skin even during human joint bending. The smart textile device exhibits excellent electro-thermal and photo-thermal conversion performance with good cycling stability and tunability. Furthermore, the textile electronics show good electromagnetic interference (EMI) shielding properties due to the good electrical conductivity, as well as sensitive and stable pressure sensing properties for human motion detection. Consequently, this efficient strategy provides a possible way to design multifunctional and wearable electronic textiles for medical applications.

Emerging 2D Metal Oxides: From Synthesis to Device Integration.

2D metal oxides have aroused increasing attention in the field of electronics and optoelectronics due to their intriguing physical properties. In this review, an overview of recent advances on synthesis of 2D metal oxides and their electronic applications is presented. First, the tunable physical properties of 2D metal oxides that relate to the structure (various oxidation-state forms, polymorphism, etc.), crystallinity and defects (anisotropy, point defects, and grain boundary), and thickness (quantum confinement effect, interfacial effect, etc.) are discussed. Then, advanced synthesis methods for 2D metal oxides besides mechanical exfoliation are introduced and classified into solution process, vapor-phase deposition, and native oxidation on a metal source. Later, the various roles of 2D metal oxides in widespread applications, i.e., transistors, inverters, photodetectors, piezotronics, memristors, and potential applications (solar cell, spintronics, and superconducting devices) are discussed. Finally, an outlook of existing challenges and future opportunities in 2D metal oxides is proposed.

Mapping of Spatiotemporal Auricular Electrophysiological Signals Reveals Human Biometric Clusters.

Underneath the ear skin there are rich vascular network and sensory nerve branches. Hence, the 3D mapping of auricular electrophysiological signals can provide new biomedical perspectives. However, it is still extremely challenging for current sensing techniques to cover the entire ultra-curved auricle. Here, a 3D graphene-based ear-conformable sensing device with embedded and distributed 3D electrodes for full-auricle physiological monitoring is reported. As a proof-of-concept, spatiotemporal auricular electrical skin resistance (AESR) mapping is demonstrated for the first time, and human subject-specific AESR distributions are observed. From the data of more than 30 ears (both right and left ears), the auricular region-specific AESR changes after cycling exercise are observed in 98% of the tests and are clustered into four groups via machine learning-based data analyses. Correlations of AESR with heart rate and blood pressure are also studied. This 3D electronic platform and AESR-based biometrical findings show promising biomedical applications.

Wide-Bandwidth Nanocomposite-Sensor Integrated Smart Mask for Tracking Multiphase Respiratory Activities.

Wearing masks has been a recommended protective measure due to the risks of coronavirus disease 2019 (COVID-19) even in its coming endemic phase. Therefore, deploying a "smart mask" to monitor human physiological signals is highly beneficial for personal and public health. This work presents a smart mask integrating an ultrathin nanocomposite sponge structure-based soundwave sensor (≈400 µm), which allows the high sensitivity in a wide-bandwidth dynamic pressure range, i.e., capable of detecting various respiratory sounds of breathing, speaking, and coughing. Thirty-one subjects test the smart mask in recording their respiratory activities. Machine/deep learning methods, i.e., support vector machine and convolutional neural networks, are used to recognize these activities, which show average macro-recalls of ≈95% in both individual and generalized models. With rich high-frequency (≈4000 Hz) information recorded, the two-/tri-phase coughs can be mapped while speaking words can be identified, demonstrating that the smart mask can be applicable as a daily wearable Internet of Things (IoT) device for respiratory disease identification, voice interaction tool, etc. in the future. This work bridges the technological gap between ultra-lightweight but high-frequency response sensor material fabrication, signal transduction and processing, and machining/deep learning to demonstrate a wearable device for potential applications in continual health monitoring in daily life.

A Wide-bandwidth Nanocomposite-Sensor Integrated Smart Mask for Tracking Multi-phase Respiratory Activities for COVID-19 Endemic

A global sentiment in early 2022 is that the COVID-19 virus could become endemic just like common cold flu viruses soon. The most optimistic view is that, with minimal precautions, such as vaccination, boosters and optional masking, life for most people will proceed as normal soon. However, as warned by A. Katzourakis of Oxford University recently [1], we must set aside lazy optimism, and must be realistic about the likely levels of death, disability and sickness that will be brought on by a COVID-19 endemic. Moreover, the world must also consider that continual circulation of the virus could give rise to new variants such as the new BA.2 variant (a subvariant of Omicron) continues to spread across the US and parts of Europe. Data from the CDC is already showing that BA.2 has been tripling in prevalence every two weeks [2]. Hence, globally, we must use available and proven weapons to continue to fight the COVID-19 viruses, i.e., effective vaccines, antiviral medications, diagnostic tests and stop an airborne virus transmission through social distancing, and mask wearing. For this work, we have demonstrated a smart mask with an optimally-coupled ultra-thin flexible soundwave sensors for tracking, classifying, and recognizing different respiratory activities, including breathing, speaking, and two-/tri-phase coughing; the masks functionality can also be augmented in the future to monitor other human physiological signals. Although researchers have integrated sensors into masks to detect respiratory activities in the past, they only based on measuring temperature and air flow during coughing, i.e., counting only the number of coughs. However, coughing is a process consisting of several phases, including an explosion of the air with glottal opening producing some noise-like waveform, a decrease of airflow to decrease sound amplitude, and a voiced stage which is the interruption of the air flow due to the closure of glottal and periodical vibration of partly glottis, which is not always present. Therefore, sensors used for cough detection should not be only sensitive to subtle air pressure but also the high-frequency vibrations, i.e., a pressure sensor that needs to be responsive to a wide input amplitude and bandwidth range, in order to detect air flows between hundreds of hertz from breath, and acoustic signals from voice that could reach [~] 8000 Hz. Respiratory activities data from thirty-one (31) human subjects were collected. Machine learning methods such as Support Vector Machines and Convolutional Neural Networks were used to classify the collected sensor data from the smart mask, which show an overall macro-recall of about 93.88% for the three respiratory sounds among all 31 subjects. For individual subjects, the 31 human subjects have the average macro-recall of 95.23% (ranging from 90% to 100%) for these 3 respiratory activities. Our work bridges the technological gap between ultra-lightweight but high-frequency response sensor material fabrication, signal transduction and conditioning, and applying machining learning algorithms to demonstrate a reliable wearable device for potential applications in continual healthy monitoring of subjects with cough symptoms during the eventual COVID-19 endemic. The monitoring and analysis of cough sound should be highly beneficial for human health management. These health monitoring data could then be shared with doctors via cloud storage and transmission technique to help disease diagnosis more effectively. Also, communication barriers caused by wearing masks can be alleviated by combining with the speech recognition techniques. In general, this research helps to advance the wearable device technology for tracking respiratory activities, similar to an Apple Watch or a Fitbit smartwatch in tracking physical and physiological activities.

Probing the Effect of MWCNT Nanoinclusions on the Thermoelectric Performance of Cu3SbS4 Composites.

Recently, copper-based chalcogenides, especially sulfides, have attracted considerable attention due to their inexpensive, earth-abundance, nontoxicity, and good thermoelectric performance. Cu3SbS4 is one such kind with p-type conductivity and high phase stability for potential medium-temperature applications. In this article, the effect of a multiwalled carbon nanotube (MWCNT) on the thermoelectric parameters of Cu3SbS4 is studied. A facile synthesis route of mechanical alloying (MA), followed by hot pressing (HP) was utilized to achieve dense and fine-grain samples. Adding the optimal amount of MWCNT nanoinclusions in Cu3SbS4 enhanced the Seebeck coefficient by carrier energy filtering and reduced the thermal conductivity by strong phonon scattering mechanisms. This synergistic optimization helped achieve the maximum figure of merit (ZT) of 0.43 in the 3 mol % MWCNT nanoinclusion composite sample, which is 70% higher than the pristine Cu3SbS4 at 623 K. In addition, enhancement in mechanical stability is observed with the increasing nanoinclusion concentration. Dispersion strengthening and grain boundary hardening mechanisms help improve mechanical stability in the nanocomposite samples. Apart from the enhanced mechanical stability, our study highlights that the incorporation of multiwalled CNT nanoinclusions boosted the thermoelectric performance of Cu3SbS4, and the same strategy can be extended to other next-generation and conventional thermoelectric materials.

Biosensors and Point-of-Care Devices for Bacterial Detection: Rapid Diagnostics Informing Antibiotic Therapy.

With an exponential rise in antimicrobial resistance and stagnant antibiotic development pipeline, there is, more than ever, a crucial need to optimize current infection therapy approaches. One of the most important stages in this process requires rapid and effective identification of pathogenic bacteria responsible for diseases. Current gold standard techniques of bacterial detection include culture methods, polymerase chain reactions, and immunoassays. However, their use is fraught with downsides with high turnaround time and low accuracy being the most prominent. This imposes great limitations on their eventual application as point-of-care devices. Over time, innovative detection techniques have been proposed and developed to curb these drawbacks. In this review, a systematic summary of a range of biosensing platforms is provided with a strong focus on technologies conferring high detection sensitivity and specificity. A thorough analysis is performed and the benefits and drawbacks of each type of biosensor are highlighted, the factors influencing their potential as point-of-care devices are discussed, and the authors' insights for their translation from proof-of-concept systems into commercial medical devices are provided.