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    Flexible PDMS Composite Electrodes with Boronic Acid-Modified Carbon Dots for Surface Electrophysiological Signal Recording
    (ACS Publications, 2024) Ali, Amaal Abdulraqeb; Al-Sayah, Mohammad H.; Al-Othman, Amani; Al Nashash, Hasan
    Conventional surface electrodes are composed of rigid metals such as Ag/AgCl that are not only harsh to the skin but also irritating if used as wet electrodes. Furthermore, rigid, inflexible surface electrodes can cause patient discomfort when used for long term. To reduce the mechanical mismatch, flexible alternatives to metal electrodes are needed. This study reports the development of highly flexible composite electrodes fabricated from the conductive dopant boronic acid-modified carbon dots embedded in a polydimethylsiloxane matrix. The electrodes were characterized for their structural, electrochemical, and mechanical characteristics and ability to record electrophysiological signals. Furthermore, the composition of these electrodes was varied systematically to obtain the optimal electrochemical and mechanical properties. The best-performing electrode composed of 10% boronic acid-modified carbon dots, 16% glycerol, and 74% polydimethylsiloxane (8:1 elastomer to curing agent) had a smooth surface, a promising conductivity of 9.62×10⁻ᵌ S/cm, an impedance of 964 kΩ at 1 kHz, and a charge storage capacity of 21.4 μC/cm². This electrode had a Young’s modulus (0.0545 MPa), which is compatible with biological tissues’ elasticity. The fabricated electrodes recorded high-quality electrocardiography signals with a promising signal-to-noise ratio (SNR) of 36.75 dB that is comparable to the commercial Ag/AgCl, which had a SNR of 39.98 dB. A similarly good performance was observed with electromyography. Furthermore, the developed flexible surface electrodes maintained their ability to record high quality ECG and EMG over a period of three weeks.
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    Liposomes-Based Drug Delivery Systems of Anti-Biofilm Agents to Combat Bacterial Biofilm Formation
    (MDPI, 2023) Makhlouf, Zinb; Ali, Amaal Abdulraqeb; Al-Sayah, Mohammad H.
    All currently approved antibiotics are being met by some degree of resistance by the bacteria they target. Biofilm formation is one of the crucial enablers of bacterial resistance, making it an important bacterial process to target for overcoming antibiotic resistance. Accordingly, several drug delivery systems that target biofilm formation have been developed. One of these systems is based on lipid-based nanocarriers (liposomes), which have shown strong efficacy against biofilms of bacterial pathogens. Liposomes come in various types, namely conventional (charged or neutral), stimuli-responsive, deformable, targeted, and stealth. This paper reviews studies employing liposomal formulations against biofilms of medically salient gram-negative and gram-positive bacterial species reported recently. When it comes to gram-negative species, liposomal formulations of various types were reported to be efficacious against Pseudomonas aeruginosa, Escherichia coli, Acinetobacter baumannii, and members of the genera Klebsiella, Salmonella, Aeromonas, Serratia, Porphyromonas, and Prevotella. A range of liposomal formulations were also effective against gram-positive biofilms, including mostly biofilms of Staphylococcal strains, namely Staphylococcus aureus, Staphylococcus epidermidis, and Staphylococcus saprophyticus subspecies bovis, followed by Streptococcal strains (pneumonia, oralis, and mutans), Cutibacterium acnes, Bacillus subtilis, Mycobacterium avium, Mycobacterium avium subsp. hominissuis, Mycobacterium abscessus, and Listeria monocytogenes biofilms. This review outlines the benefits and limitations of using liposomal formulations as means to combat different multidrug-resistant bacteria, urging the investigation of the effects of bacterial gram-stain on liposomal efficiency and the inclusion of pathogenic bacterial strains previously unstudied.
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    The Development of Metal-Free Porous Organic Polymers for Sustainable Carbon Dioxide Photoreduction
    (MDPI, 2024) Bariki, Ranjit; Joseph, Reshma G.; El-Kadri, Oussama M.; Al-Sayah, Mohammad H.
    A viable tactic to effectively address the climate crisis is the production of renewable fuels via photocatalytic reactions using solar energy and available resources like carbon dioxide (CO₂) and water. Organic polymer material-based photocatalytic materials are thought to be one way to convert solar energy into valuable chemicals and other solar fuels. The use of porous organic polymers (POPs) for CO₂ fixation and capture and sequestration to produce beneficial compounds to reduce global warming is still receiving a lot of interest. Visible light-responsive organic photopolymers that are functionally designed and include a large number of heteroatoms and an extended π-conjugation allow for the generation of photogenerated charge carriers, improved absorption of visible light, increased charge separation, and decreased charge recombination during photocatalysis. Due to their rigid structure, high surface area, flexible pore size, permanent porosity, and adaptability of the backbone for the intended purpose, POPs have drawn more and more attention. These qualities have been shown to be highly advantageous for numerous sustainable applications. POPs may be broadly categorized as crystalline or amorphous according to how much long-range order they possess. In terms of performance, conducting POPs outperform inorganic semiconductors and typical organic dyes. They are light-harvesting materials with remarkable optical characteristics, photostability, cheap cost, and low cytotoxicity. Through cocatalyst loading and morphological tweaking, this review presents optimization options for POPs preparation techniques. We provide an analysis of the ways in which the preparative techniques will affect the materials’ physicochemical characteristics and, consequently, their catalytic activity. An inventory of experimental methods is provided for characterizing POPs’ optical, morphological, electrochemical, and catalytic characteristics. The focus of this review is to thoroughly investigate the photochemistry of these polymeric organic photocatalysts with an emphasis on understanding the processes of internal charge generation and transport within POPs. The review covers several types of amorphous POP materials, including those based on conjugated microporous polymers (CMPs), inherent microporosity polymers, hyper-crosslinked polymers, and porous aromatic frameworks. Additionally, common synthetic approaches for these materials are briefly discussed.
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    Test 1 20240902
    (2024) Lecat, Véronique
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    Hand-Crafted Features With A Simple Deep Learning Architecture For Sensor-Based Human Activity Recognition
    (IEEE, 2024-07-10) Albadawi, Yaman; Shanableh, Tamer
    With the growth in the wearable device market, wearable sensor-based human activity recognition systems have been gaining increasing interest in research because of their rising demands in many areas. This research presents a novel sensor-based human activity recognition system that utilizes a unique feature extraction technique associated with a deep learning method for classification. One of the main contributions of this work is dividing the sensor sequences time-wise into non-overlapping 2D segments. Then, statistical features are computed from each 2D segment using two approaches; the first approach computes features from the raw sensor readings, while the second approach applies time-series differencing to sensor readings prior to feature calculations. Applying time-series differencing to 2D segments helps in identifying the underlying structure and dynamics of the sensor reading across time. This work experiments with different numbers of 2D segments of sensor reading sequences. Also, it reports results with and without the use of different components of the proposed system. Additionally, it analyses the best-performing models’ complexity, comparing them with other models trained by integrating the proposed method with an existing transformer network. All of these arrangements are tested with different deep-learning architectures supported by an attention layer to enhance the model. Four benchmark datasets are used to perform several experiments, namely, mHealth, USC-HAD, UCI-HAR, and DSA. The experimental results revealed that the proposed system outperforms human activity recognition rates reported in the most recent studies. Specifically, this work reports recognition rates of 99.17%, 81.07%, 99.44%, and 94.03% for the four datasets, respectively.

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