Bio-nanotechnology application in wastewater treatment

The nanoparticles have received high interest in the field of medicine and water purification, however, the nanomaterials produced by chemical and physical methods are considered hazardous, expensive, and leave behind harmful substances to the environment. This chapter aimed to focus on green-synthesized nanoparticles and their medical applications. Moreover, the chapter highlighted the applicability of the metallic nanoparticles (MNPs) in the inactivation of microbial cells due to their high surface and small particle size. Modifying nanomaterials produced by green-methods is safe, inexpensive, and easy. Therefore, the control and modification of nanoparticles and their properties were also discussed

Antimicrobial lubricant formulations containing poly(hydroxybenzene)-trimethoprim conjugates synthesized by tyrosinase

Poly(hydroxybenzene)-trimethoprim conjugates were prepared using methylparaben as substrate of the oxida- tive enzyme tyrosinase. MALDI-TOF MS analysis showed that the enzymatic oxidation of methylparaben alone leads to the poly(hydroxybenzene) formation. In the presence of tri- methoprim, the methylparaben tyrosinase oxidation leads poly(hydroxybenzene)-trimethoprim conjugates. All of these compounds were incorporated into lubricant hydroxyethyl cellulose/glycerol mixtures. Poly(hydroxybenzene)-trimetho- prim conjugates were the most effective phenolic structures against the bacterial growth reducing by 96 and 97 % of Escherichia coli and Staphylococcus epidermidis suspen- sions, respectively (after 24 h). A novel enzymatic strategy to produce antimicrobial poly(hydroxybenzene)-antibiotic conjugates is proposed here for a wide range of applications on the biomedical field.The authors Idalina Gonçalves and Cláudia Botelho would like to acknowledge the NOVO project (FP7-HEALTH- 2011.2.3.1- 5) for funding. Loïc Hilliou acknowledges the financial support by FCT – Foundation for Science and Technology, Portugal (501100001871), through Grant PEst-C/CTM/LA0025/2013 - Strategic Project - LA 25 - 2013–2014, and by Programa Operacional Regional do Norte (ON.2) through the project BMatepro – Optimizing Materials and Processes^, with reference NORTE-07-0124-FEDER-000037 FEDER COMPETE

Shear wave velocity prediction using seismic attributes and well log data

Formation’s properties can be estimated indirectly using joint analysis of compressional and shear wave velocities. Shear wave data isnot usually acquired during well logging, which is most likely for costsaving purposes. Even if shear data is available, the logging programs provide only sparsely sampled one-dimensional measurements: this informationis inadequate to estimate reservoir rock properties. Thus, if the shear wave data can be obtained using seismic methods, the results can be used across the field to estimate reservoir properties. The aim of this paper is to use seismic attributes for prediction of shear wave velocity in a field located in southern part of Iran. Independent component analysis(ICA) was used to select the most relevant attributes to shear velocity data. Considering the nonlinear relationship between seismic attributes and shear wave velocity, multi-layer feed forward neural network was used for prediction of shear wave velocity and promising results were presented

Observation of electroweak W+W− pair production in association with two jets in proton-proton collisions at √s = 13 TeV

Copyright © 2022 The Author(s). An observation is reported of the electroweak production of a W+W− pair in association with two jets, with both W bosons decaying leptonically. The data sample corresponds to an integrated luminosity of 138 fb−1 of proton-proton collisions at √s = 13 TeV, collected by the CMS detector at the CERN LHC.Events are selected by requiring exactly two opposite-sign leptons (electrons or muons) and two jets with large pseudorapidity separation and high dijet invariant mass. Events are categorized based on the flavor of the final-state leptons. A signal is observed with a significance of 5.6 standard deviations (5.2 expected) with respect to the background-only hypothesis. The measured fiducial cross section is 10.2 ± 2.0 fb and this value is consistent with the standard model prediction of 9.1 ± 0.6 fb.SCOAP3