Enhanced thermoelectric properties of flexible aerosol-jet printed carbon nanotube-based nanocomposites

Aerosol-jet printing allows functional materials to be printed from inks with a wide range of viscosities and constituent particle sizes onto various substrates, including the printing of organic thermoelectric materials on flexible substrates for low-grade thermal energy harvesting. However, these materials typically suffer from relatively poor thermoelectric performance, compared to traditional inorganic counterparts, due to their low Seebeck coefficient, S, and electrical conductivity, σ. Here, we demonstrate a modified aerosol-jet printing technique that can simultaneously incorporate well dispersed high S Sb2Te3 nanoflakes, and high-σ multi-walled carbon nanotubes (MWCNTs) providing good inter-particle connectivity, to significantly enhance the thermoelectric performance of poly(3,4-ethylenedioxythiophene) polystyrene sulfonate (PEDOT:PSS) structures on flexible polyimide substrates. A nominal loading fraction of 85 wt.% yielded a power factor of ~41 µW/mK2, which is among the highest for printed organic-based structures. Rigorous flexing and fatigue tests were performed to confirm the robustness and stability of these aerosol-jet printed MWCNT-based thermoelectric nanocomposites

LEADNet: Detection of Alzheimer’s Disease using Spatiotemporal EEG Analysis and Low-Complexity CNN

© 2024 The Author(s). This is an open access article under the Creative Commons Attribution-Non Commercial-No Derivatives CC BY-NC-ND licence, https://creativecommons.org/licenses/by-nc-nd/4.0/Clinical methods for dementia detection are expensive and prone to human errors. Despite various computer-aided methods using electroencephalography (EEG) signals and artificial intelligence, a reliable detection of Alzheimer’s disease (AD) remains a challenge. The existing EEG-based machine learning models have limited performance or high computation complexity. Hence, there is a need for an optimal deep learning model for the detection of AD. This paper proposes a low-complexity EEG-based AD detection CNN called LEADNet to generate disease-specific features. LEADNet employs spatiotemporal EEG signals as input, two convolution layers for feature generation, a max-pooling layer for asymmetric spatiotemporal redundancy reduction, two fully-connected layers for nonlinear feature transformation and selection, and a softmax layer for disease probability prediction. Different quantitative measures are calculated using an open-source AD dataset to compare LEADNet and four pre-trained CNN models. The results show that the lightweight architecture of LEADNet has at least a 150-fold reduction in network parameters and the highest testing accuracy of 99.24% compared to pre-trained models. The investigation of individual layers of LEADNet showed successive improvements in feature transformation and selection for detecting AD subjects. A comparison with the state-of-the-art AD detection models showed that the highest accuracy, sensitivity, and specificity were achieved by the LEADNet model.Peer reviewe

Very High Surface Area Mesoporous Thin Films of SrTiO3_{3} Grown by Pulsed Laser Deposition and Application to Efficient Photoelectrochemical Water Splitting

Very high surface area, self-assembled, highly crystalline mesoporous SrTiO$_{3}$ (STO) thin films were developed for photoelectrochemical water splitting. Much improved performance of these mesoporous films compared to planar STO thin films and any other form of STO such as single crystal samples and nanostructures was demonstrated. The high performance resulted from very large surface area films and optimization of carrier concentration.We gratefully acknowledge the support from the Cambridge Commonwealth Trust, ERC adg grant (247276) NOVOX and UKIERI grant (IND/CONT/E/12-13/813). The TEM work at Texas A&M University is funded by the US National Science Foundation (DMR-1401266)

LCADNet: A Novel Light CNN Architecture for EEG-based Alzheimer Disease Detection

© 2024 Australasian College of Physical Scientists and Engineers in Medicine. This is the accepted manuscript version of an article which has been published in final form at https://doi.org/10.1007/s13246-024-01425-wAlzheimer’s disease (AD) is a progressive and incurable neurologi- cal disorder with a rising mortality rate, worsened by error-prone, time-intensive, and expensive clinical diagnosis methods. Automatic AD detection methods using hand-crafted Electroencephalogram (EEG) signal features lack accuracy and reliability. A lightweight convolu- tion neural network for AD detection (LCADNet) is investigated to extract disease-specific features while reducing the detection time. The LCADNet uses two convolutional layers for extracting complex EEG features, two fully connected layers for selecting disease-specific fea- tures, and a softmax layer for predicting AD detection probability. A max-pooling layer interlaced between convolutional layers decreases the time-domain redundancy in the EEG signal. The efficiency of the LCADNet and four pre-trained models using transfer learning is com- pared using a publicly available AD detection dataset. The LCADNet shows the lowest computation complexity in terms of both the num- ber of floating point operations and inference time and the highest classification performance across six measures. The generalization of the LCADNet is assessed by cross-testing it with two other publicly available AD detection datasets. It outperforms existing EEG-based AD detection methods with an accuracy of 98.50%. The LCADNet may be a valuable aid for neurologists and its Python implemen- tation can be found at github.com/SandeepSangle12/LCADNet.git.Peer reviewe

Novel molecular aberrations and pathologic findings in a tubulocystic variant of renal cell carcinoma

Tubulocystic renal cell carcinoma (TRCC) is an indolent type of renal cell carcinoma with a good prognosis based on the limited number of published cases. Herein, we describe the unusual clinical, pathologic and molecular findings in a case of TRCC. Our patient with TRCC had two local recurrences and a brain metastasis following radical nephrectomy. Unusual histologic findings included focal solid growth pattern and cytologic atypia. A genome-wide molecular inversion probe assay identified copy number (CN) loss in three chromosome regions and one region with copy-neutral loss of heterozygosity (copy-neutral LOH). Copy number variations (CNVs) were observed (chromosomes 4p16.1 and 17q21.31-q21.32) in both the tumor and the normal tissue, and most likely represents benign variations. The loss of entire chromosomes 9, 18 and 15 and copy-neutral LOH involving 6p22.1 was observed only in the tumor. The presence of these clinical, pathologic and molecular findings could be related to an increased risk for tumor recurrence and poor prognosis. The novel molecular findings described in TRCC might represent new targets for novel therapies