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.

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.

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.