Amine-Decorated Methacrylic Acid-based Inverse Vulcanized Polysulfide for Effective Mercury Removal from Wastewater

Mercury [Hg(II)] contamination is an indefatigable global hazard that causes severe permanent damage to human health. Extensive research has been carried out to produce mercury adsorbents; however, they still face certain challenges, limiting their upscaling. Herein, we report the synthesis of a novel amine-impregnated inverse vulcanized copolymer for effective mercury removal. Poly(S-MA) was prepared using sulfur and methacrylic acid employing the inverse vulcanization method, followed by functionalization. The polyethylenimine (PEI) was impregnated on poly(S-MA) to increase the adsorption active sites. The adsorbent was then characterized byusing Fourier transform infrared (FTIR) spectroscopy and scanning electron microscopy (SEM). FTIR spectroscopy confirmed the formation of the copolymer, and successful impregnation of PEI and SEM revealed the composite porous morphology of the copolymer. Amine-impregnated copolymer [amine@poly(S-MA)] outperformed poly(S-MA) in mercury as it showed 20% superior performance with 44.7 mg/g of mercury adsorption capacity. The adsorption data best fit the pseudo-second-order, indicating that chemisorption is the most effective mechanism, in this case, indicating the involvement of NH2 in mercury removal. The adsorption is mainly a monolayer on a homogeneous surface as indicated by the 0.76 value of Redlich-Peterson exponent (g), which describes the adsorption nature advent from the R2 value of 0.99.

A Novel Image Processing Method for Obtaining an Accurate Three-Dimensional Profile of Red Blood Cells in Digital Holographic Microscopy

Recently, research on disease diagnosis using red blood cells (RBCs) has been active due to the advantage that it is possible to diagnose many diseases with a drop of blood in a short time. Representatively, there are disease diagnosis technologies that utilize deep learning techniques and digital holographic microscope (DHM) techniques. However, three-dimensional (3D) profile obtained by DHM has a problem of random noise caused by the overlapping DC spectrum and sideband in the Fourier domain, which has the probability of misjudging diseases in deep learning technology. To reduce random noise and obtain a more accurate 3D profile, in this paper, we propose a novel image processing method which randomly selects the center of the high-frequency sideband (RaCoHS) in the Fourier domain. This proposed algorithm has the advantage of filtering while using only recorded hologram information to maintain high-frequency information. We compared and analyzed the conventional filtering method and the general image processing method to verify the effectiveness of the proposed method. In addition, the proposed image processing algorithm can be applied to all digital holography technologies including DHM, and in particular, it is expected to have a great effect on the accuracy of disease diagnosis technologies using DHM.

Insights into bacterial community metatranscriptome and metabolome in river water influenced by palm oil mill effluent final discharge

AIMS

This study aimed to investigate the effect of palm oil mill effluent (POME) final discharge on the active bacterial composition, gene expression, and metabolite profiles in the receiving rivers to establish a foundation for identifying potential biomarkers for monitoring POME pollution in rivers.

METHODS AND RESULTS

The POME final discharge, upstream (unpolluted by POME), and downstream (effluent receiving point) parts of the rivers from two sites were physicochemically characterized. The taxonomic and gene profiles were then evaluated using de novo metatranscriptomics, while the metabolites were detected using qualitative metabolomics. A similar bacterial community structure in the POME final discharge samples from both sites was recorded, but their composition varied. Redundancy analysis showed that several families, particularly Comamonadaceae and Burkholderiaceae [Pr(>F) = 0.028], were positively correlated with biochemical oxygen demand (BOD5) and chemical oxygen demand (COD). The results also showed significant enrichment of genes regulating various metabolisms in the POME-receiving rivers, with methane, carbon fixation pathway, and amino acids among the predominant metabolisms identified (FDR < 0.05, PostFC > 4, and PPDE > 0.95). This was further validated through qualitative metabolomics, whereby amino acids were detected as the predominant metabolites.

CONCLUSIONS

The results suggest that genes regulating amino acid metabolism have significant potential for developing effective biomonitoring and bioremediation strategies in river water influenced by POME final discharge, fostering a sustainable palm oil industry.

Three-Dimensional (3D) Visualization under Extremely Low Light Conditions Using Kalman Filter

In recent years, research on three-dimensional (3D) reconstruction under low illumination environment has been reported. Photon-counting integral imaging is one of the techniques for visualizing 3D images under low light conditions. However, conventional photon-counting integral imaging has the problem that results are random because Poisson random numbers are temporally and spatially independent. Therefore, in this paper, we apply a technique called Kalman filter to photon-counting integral imaging, which corrects data groups with errors, to improve the visual quality of results. The purpose of this paper is to reduce randomness and improve the accuracy of visualization for results by incorporating the Kalman filter into 3D reconstruction images under extremely low light conditions. Since the proposed method has better structure similarity (SSIM), peak signal-to-noise ratio (PSNR) and cross-correlation values than the conventional method, it can be said that the visualization of low illuminated images can be accurate. In addition, the proposed method is expected to accelerate the development of autonomous driving technology and security camera technology.