Millions of tons of paper and its derivatives are annually wasted without being recycled and reused. To promote the comprehensive utilization of resources and eco-friendly preparation, waste filter paper, printer paper, and napkins were chosen as carbon sources to one-step synthesize three types of three dimensional (3D) net-like magnetic mesoporous carbon (MMC) by an eco-friendly and low-cost method. These mesoporous (3.90-7.68 nm) composites have a high specific surface area (287-423 m g), well-developed porosity (0.24-0.74 cm g) and abundant oxygen-containing functional groups. Compared to the other two composites, the adsorbent derived from filter paper showed the highest adsorption capacity towards methylene blue (MB) ( = 332.03 mg g) and rhodamine B (RhB) ( = 389.59 mg g) with a high adsorption rate (<5 min). According to the effect of pH value on adsorption capacity, and combining the analysis of Fourier transform infrared spectrometry and X-ray photoelectron spectroscopy, the main adsorption mechanisms can be summarized as hydrogen bonds, electrostatic interactions, and π-π interaction. Besides, the occurrence of redox reactions between Fe/Fe and dye cannot be ignored. Finally, experiments on reusability were performed. They showed that the 3D net-like MMC could be easily regenerated and still maintained a removal efficiency of above 80% for RhB and 90% for MB after five cycles.
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http://dx.doi.org/10.1039/c9ra01332f | DOI Listing |
Med Phys
January 2025
Institute of Modern Physics, Chinese Academy of Sciences, Lanzhou, China.
Background: Online adaptive radiotherapy (OART) and rapid quality assurance (QA) are essential for effective heavy ion therapy (HIT). However, there is a shortage of deep learning (DL) models and workflows for predicting Monte Carlo (MC) doses in such treatments.
Purpose: This study seeks to address this gap by developing a DL model for independent MC dose (MCDose) prediction, aiming to facilitate OART and rapid QA implementation for HIT.
Nat Methods
January 2025
Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada.
Teravoxel-scale, cellular-resolution images of cleared rodent brains acquired with light-sheet fluorescence microscopy have transformed the way we study the brain. Realizing the potential of this technology requires computational pipelines that generalize across experimental protocols and map neuronal activity at the laminar and subpopulation-specific levels, beyond atlas-defined regions. Here, we present artficial intelligence-based cartography of ensembles (ACE), an end-to-end pipeline that employs three-dimensional deep learning segmentation models and advanced cluster-wise statistical algorithms, to enable unbiased mapping of local neuronal activity and connectivity.
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January 2025
Broad Institute of MIT and Harvard, Cambridge, MA, USA.
A key challenge of the modern genomics era is developing empirical data-driven representations of gene function. Here we present the first unbiased morphology-based genome-wide perturbation atlas in human cells, containing three genome-wide genotype-phenotype maps comprising CRISPR-Cas9-based knockouts of >20,000 genes in >30 million cells. Our optical pooled cell profiling platform (PERISCOPE) combines a destainable high-dimensional phenotyping panel (based on Cell Painting) with optical sequencing of molecular barcodes and a scalable open-source analysis pipeline to facilitate massively parallel screening of pooled perturbation libraries.
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January 2025
Department of Artificial Intelligence and Data Science, College of Computer Science and Engineering, University of Hail, Hail, Saudi Arabia.
In the present digital scenario, the explosion of Internet of Things (IoT) devices makes massive volumes of high-dimensional data, presenting significant data and privacy security challenges. As IoT networks enlarge, certifying sensitive data privacy while still employing data analytics authority is vital. In the period of big data, statistical learning has seen fast progressions in methodological practical and innovation applications.
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January 2025
College of Geosciences and Engineering, North China University of Water Resources and Electric Power, Zhengzhou, 450046, China.
The failure of locked-segment landslides is associated with the destruction of locked segments that exhibit an energy accumulation effect. Thus, understanding their failure mode and instability mechanism for landslide hazard prevention and control is critical. In this paper, multiple instruments, such as tilt sensors, pore water pressure gauges, moisture sensors, matrix suction sensors, resistance strain gauges, miniature earth pressure sensors, a three-dimensional (3D) laser scanner, and a camera, were used to conduct the physical model tests on the rainfall-induced arch locked-segment landslide to analyze the resulting tilting deformation and evolution mechanism.
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