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Neurological disorders such as Parkinson's disease (PD) often adversely affect the vascular system, leading to alterations in blood flow patterns. Functional near‐infrared spectroscopy (fNIRS) is used to monitor hemodynamic changes via signal measurement. This study investigated the potential of using resting‐state fNIRS data through a convolutional neural network (CNN) to evaluate PD with orthostatic hypotension. The CNN demonstrated significant efficacy in analyzing fNIRS data, and it outperformed the other machine learning methods. The results indicate that judicious input data selection can enhance accuracy by over 85%, while including the correlation matrix as an input further improves the accuracy to more than 90%. This study underscores the promising role of CNN‐based fNIRS data analysis in the diagnosis and management of the PD. This approach enhances diagnostic accuracy, particularly in resting‐state conditions, and can reduce the discomfort and risks associated with current diagnostic methods, such as the head‐up tilt test. [ABSTRACT FROM AUTHOR]

Two‐terminal‐based artificial synapses have attracted attention because their electronic properties can be applied to next‐generation computing. Herein, interfacial phase‐change memory (iPCM) devices based on sputter‐grown GeTe/Sb2Te3 are fabricated. The iPCM device exhibits excellent multilevel resistance switching via control of entropy by restricting the movement of Ge atoms. Based on this movement, the optimal pulse scheme and GeTe/Sb2Te3 layer are used to implement a tunable analog weight update of artificial synapses. The nonlinearity of 0.32 and 40 conductance states (GeTe/Sb2Te3)16 iPCM is achieved for long‐term potentiation and depression, respectively. This artificial synapse, which stably changes the gradual conductance value, has the potential for significant performance improvement of neuromorphic computing. [ABSTRACT FROM AUTHOR]

The multi‐level feature of GeTe/Sb2Te3 interfacial phase change memory was achieved by applying a designed voltage‐based pulse. It stably demonstrated five multi‐level states without interference for 90 cycles by varying the pulse width. GeTe/Sb2Te3 interfacial phase change memory demonstrated retention time of > 1.0 × 103 s, presenting the significantly low drift coefficient (ν) of < 0.009, indicating no resistivity drift due to the structure relaxation of glass. In addition, the reset energy consumption of GeTe/Sb2Te3 interfacial phase change memory was reduced by more than 85% compared to conventional Ge2Sb2Te5 phase change memory at each bottom electrode contact size. Multi‐level‐cell operation mechanism and gradual increase in conductance value of GeTe/Sb2Te3 interfacial phase change memory was explained by a partial resistance transition model where phase transition occurred partially in all layers. The result of the GeTe/Sb2Te3 interfacial phase change memory performance is expected to bring great advantages to the next‐generation storage class memory industry that requires low energy and high density. [ABSTRACT FROM AUTHOR]

The so‐called colloidal template synthesis has been applied to the preparation of surface‐engineered nanoadsorbents. Colloidal microporous organic network nanotemplates (C‐MONs), which showed a high surface area (611 m2 g−1) and enhanced microporosity, were prepared through the networking of organic building blocks in the presence of poly(vinylpyrrolidone) (PVP). Owing to entrapment of the PVP in networks, the C‐MONs showed good colloidal dispersion in EtOH. MoS2 precursors were incorporated into the C‐MONs and heat treatment afforded core–shell‐type C@MoS2 nanoparticles with a diameter of 80 nm, a negative zeta potential (−39.5 mV), a high surface area (508 m2 g−1), and excellent adsorption performance towards cationic dyes (qmax=343.6 and 421.9 mg g−1 for methylene blue and rhodamine B, respectively). [ABSTRACT FROM AUTHOR]