To investigate the feasibility of utilizing low strength electric fields to transport commonly available mixed cultures such as those from an activated sludge process, bench scale batch reactor studies were conducted in sand and sandy loam soils. A readily biodegradable substrate, dextrose, was used to test the activity of the transported microorganisms. Electric field strengths of 7V, 10.5V, and 14V were used. Results from this investigation showed that an electric field strength of 0.46 Volts per cm was sufficient to transport activated sludge microorganisms across a sandy loam soil across a distance of about 8 cm in 72 h. More importantly, the electrokinetically transported microbial culture remained active and viable after the transport process and was biodegrade 44% of the dextrose in the soil medium. Electrokinetic treatment without microorganisms resulted in removal of 37% and the absence of any treatment yielded a removal of about 15%.
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http://dx.doi.org/10.1080/10934529.2011.562832 | DOI Listing |
Sci Rep
January 2025
Mechanical and Industrial Engineering Department, Universitas Gadjah Mada, Yogyakarta, Indonesia.
Research on flexible strain sensors has grown rapidly and is widely applied in the fields of soft robotics, body motion detection, wearable sensors, health monitoring, and sports. In this study, MXene was successfully synthesized in powder form and combined with multi-walled carbon nanotube (MWCNT) to develop MWCNT@MXene conductive network-based flexible strain sensors with silicone rubber (SR) substrate. Combining MWCNTs with MXene as a conductive material has been shown to significantly improve the sensor performance, due to MXene's high conductivity properties that strengthen the MWCNT conductive pathway, increase sensitivity, and improve sensor stability.
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January 2025
Nanoelectronics Integrated Systems Center (NISC), Nile University, Giza, 12677, Egypt.
Due to its enormous influence on system functionality, researchers are presently looking into the issue of task scheduling on multiprocessors. Establishing the most advantageous schedules is often regarded as a difficult-to-compute issue. Genetic Algorithm is a recent tool employed by researchers to optimize scheduling tasks and boost performance, although this field of research is yet mostly unexplored.
View Article and Find Full Text PDFNat Commun
January 2025
Institut de Chimie de Strasbourg (UMR 7177, CNRS-Unistra), Université de Strasbourg, 4 rue Blaise Pascal, CS 90032, F-, Strasbourg, France.
Electric fields represent an ideal means for controlling spins at the nanoscale and, more specifically, for manipulating protected degrees of freedom in multispin systems. Here we perform low-temperature magnetic far-IR spectroscopy on a molecular spin triangle (Fe) and provide initial experimental evidence suggesting spin-electric transitions in polynuclear complexes. The co-presence of electric- and magnetic-dipole transitions, allows us to estimate the spin-electric coupling.
View Article and Find Full Text PDFLight Sci Appl
January 2025
Department of Electrical Engineering, City University of Hong Kong, Kowloon, Hong Kong SAR, China.
Combining bright-field and edge-enhanced imaging affords an effective avenue for extracting complex morphological information from objects, which is particularly beneficial for biological imaging. Multiplexing meta-lenses present promising candidates for achieving this functionality. However, current multiplexing meta-lenses lack spectral modulation, and crosstalk between different wavelengths hampers the imaging quality, especially for biological samples requiring precise wavelength specificity.
View Article and Find Full Text PDFJ Imaging Inform Med
January 2025
Ear, Nose and Throat Department, Batman Training and Research Hospital, Batman, Turkey.
Adenoid hypertrophy, characterized by the abnormal enlargement of adenoid tissue, is a condition that can cause significant breathing and sleep disturbances, particularly in children. Accurate diagnosis of adenoid hypertrophy is critical, yet traditional methods, such as imaging and manual interpretation, are prone to errors. This study uses an ensemble deep learning-based approach for adenoid classification.
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