Low switching power neuromorphic perovskite devices with quick relearning functionality

In the quest to reduce energy consumption, there is a growing demand for technology beyond silicon as electronic materials for neuromorphic artificial intelligence devices. Equipped with the criteria of energy efficiency and excellent adaptability, organohalide perovskites can emulate the characteristics of synaptic functions in the human brain. In this aspect, this study designs and develops CsFAPbI3-based memristive neuromorphic devices that can switch at low power and show larger endurance by adopting the powder engineering methodology. The neuromorphic characteristics of the CsFAPbI3-based devices exhibit an ultra-high paired-pulse facilitation index for an applied electric stimuli pulse. Moreover, the transition from short-term to long-term memory requires ultra-low energy with long relaxation times. The learning and training cycles illustrate that the CsFAPbI3-based devices exhibit faster learning and memorization process owing to their larger carrier lifetime compared to other perovskites. The results provide a pathway to attain low-power neuromorphic devices that are synchronic to the human brain's performance

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

Facile composite engineering to boost thermoelectric power conversion in ZnSb device

Zinc antimonide (ZnSb) is one of the alternatives for commercial thermoelectric materials due to its non-toxic, low-cost, and earth-abundant nature. However, its simple crystal structure causes strong phonon vibrations, which enhance lattice thermal conductivity. In this work, we systematically studied the effect of γ-Al2O3 nano-inclusions on ZnSb. Our results show that composite engineering imparts lattice phonon scattering for reduced thermal conductivity and low-energy carrier filtering for enhanced Seebeck coefficient. The obtained figure of merit in the ZnSb+5% γ-Al2O3 sample at 673 K is nearly two-fold higher than the pristine sample. Our fabricated 2-leg ZnSb+5% γ-Al2O3 device displayed a power generation of 0.11 μW at ΔT of 200 °C. Furthermore, adding γ-Al2O3 nano-inclusions improve the mechanical and thermal stabilities due to grain boundary hardening and dispersion strengthening. Overall, the addition of γ-Al2O3 nano-inclusions to ZnSb enhancing the Seebeck coefficient, reducing thethermal conductivity, and improving mechanical and thermal stability significantly

Compact magnetic field amplification by tuned Lenz lens

High-frequency magnetic field sensing is a vital feature of several biomedical and industrial applications. Typically, highly sensitive magnetic materials are used for such applications, yet such materials are expensive and their development is bespoke. Recently, there has been an increased interest to reshape magnetic fields to design high-performance sensing devices. In this paper, we present and evaluate the design of a Lenz lens-based miniaturised magnetic field sensor onto a single substrate. We envision that our device can be used for magnetic field based sensing applications. Through simulations, we show that the introduction of a Lenz lens confines and enhances the magnetic field in the region of the sensor. Our design is validated using laboratory measurements that show a 40-fold improvement in the detected signal at 28.6 MHz when a Lenz lens is placed around the sensor. We also note that the enhancement leads to a 15 dB increase in the signal-to-noise ratio of the detected signal. We fabricated the design using etching techniques that are both well-known and low-cost, therefore, showing potential for mass-scale production. We demonstrate that it is not only possible to create a device that enhances magnetic field sensitivity, but also to do so in a manner that makes it easy to integrate with existing technologies. The latter feature allows it to become immediately useful for a variety of NMR applications

2D MXene interface engineered bismuth telluride thermoelectric module with improved efficiency for waste heat recovery

Graphene analog MXenes are the best options for interface engineering traditional thermoelectric materials. For the first time, a composite-engineered TEG device composed of heavily doped bismuth and antimony telluride with incorporated Ti3C2Tx (MXene) nanoflakes is developed. Incorporated MXenes improved the electrical conductivity by carrier injection and reduces thermal conductivity by interfacial phonon scattering in both composites. The fabricated composite TEG device resulted in a maximum power of 1.14 mW and a power density of 6.1 mWcm−2. The fabricated composite TEG also demonstrates strong power generation stability and durability. Added MXenes improve the mechanical stability by employing a dispersion-strengthening mechanism. Conclusively, the developed composite-engineered TEG device is a facile and efficiency-improving option for next-generation bismuth telluride-based commercial TEG devices

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

Circulating metabolites associated with alcohol intake in the european prospective investigation into cancer and nutrition cohort

Identifying the metabolites associated with alcohol consumption may provide insights into the metabolic pathways through which alcohol may affect human health. We studied associations of alcohol consumption with circulating concentrations of 123 metabolites among 2974 healthy participants from the European Prospective Investigation into Cancer and Nutrition (EPIC) study. Alcohol consumption at recruitment was self-reported through dietary questionnaires. Metabolite concentrations were measured by tandem mass spectrometry (BIOCRATES AbsoluteIDQTMp180 kit). Data were randomly divided into discovery (2/3) and replication (1/3) sets. Multivariable linear regression models were used to evaluate confounder-adjusted associations of alcohol consumption withmetabolite concentrations. Metabolites significantly related to alcohol intake in the discovery set (FDR q-value < 0.05) were further tested in the replication set (Bonferroni-corrected p-value < 0.05). Of the 72metabolites significantly related to alcohol intake in the discovery set, 34 were also significant in the replication analysis, including three acylcarnitines, the amino acid citrulline, four lysophosphatidylcholines, 13 diacylphosphatidylcholines, seven acyl-alkylphosphatidylcholines, and six sphingomyelins. Our results confirmed earlier findings that alcohol consumption was associated with several lipid metabolites, and possibly also with specific acylcarnitines and amino acids. This provides further leads for future research studies aiming at elucidating the mechanisms underlying the effects of alcohol in relation to morbid conditions. © 2018 by the authors