Experimental Investigation on Upgrading of Lignin‐Derived Bio‐Oils: Kinetic Analysis of Anisole Conversion on Sulfided CoMo/Al2O3 Catalyst

Kinetics of the hydroprocessing of anisole, a compound representative of lignin‐derived bio‐oils, catalyzed by a commercial sulfided CoMo/Al₂O₃, was determined at 8–20 bar pressure and 573–673 K with a once‐through flow reactor. The catalyst was sulfided in an atmosphere of H₂ + H₂S prior to the measurement of its performance. Selectivity‐conversion data were used as a basis for determining an approximate, partially quantified reaction network showing that hydrodeoxygenation (HDO), hydrogenolysis, and alkylation reactions take place simultaneously. The data indicate that these reactions can be stopped at the point where HDO is virtually completed and hydrogenation reactions (and thus H₂ consumption) are minimized. Phenol was the major product of the reactions, with direct deoxygenation of anisole to give benzene being kinetically almost insignificant under our conditions. We infer that the scission of the C_methyl–O bond is more facile than the scission of the C_aromatic–O bond, so that the HDO of anisole likely proceeds substantially through the reactive intermediate phenol to give transalkylation products such as 2‐methylphenol. The data determine rates of formation of the major primary products. The data show that if oxygen removal is the main processing goal, higher temperatures and lower pressures are favored.     

One-pot synthesis of novel pyrrolo-1,4-benzoxazines via a three- component reaction of 2-amino phenols,acetylenic esters and nitrostyrene derivatives简

A simple and efficient synthetic protocol has been developed using a one-pot, three-component reaction involving 2-amino phenols, dialkyl acetylene dicarboxylates and nitrostyrene derivatives. Utilizing this protocol, a variety of novel pyrrolo-1,4-benzoxazine derivatives were synthesized in excellent yields.     

Targeted L1-Regularization and Joint Modeling of Neural Networks for Causal Inference

The calculation of the Augmented Inverse Probability Weighting (AIPW) estimator of the Average Treatment Effect (ATE) is carried out in two steps, where in the first step, the treatment and outcome are modeled, and in the second step, the predictions are inserted into the AIPW estimator. The model misspecification in the first step has led researchers to utilize Machine Learning algorithms instead of parametric algorithms. However, the existence of strong confounders and/or Instrumental Variables (IVs) can lead the complex ML algorithms to provide perfect predictions for the treatment model which can violate the positivity assumption and elevate the variance of AIPW estimators. Thus the complexity of ML algorithms must be controlled to avoid perfect predictions for the treatment model while still learning the relationship between the confounders and the treatment and outcome. We use two NN architectures with an L1-regularization on specific NN parameters and investigate how their certain hyperparameters should be tuned in the presence of confounders and IVs to achieve a low bias-variance tradeoff for ATE estimators such as AIPW estimator. Through simulation results, we will provide recommendations as to how NNs can be employed for ATE estimation.     

Performance Improvement in Fiber-Optic Spectrometers Using Photonic Crystal Fibers

Abstract

Resolution enhancement of a high-speed fiber-optic spectrometer is investigated in this article. The operation of these types spectrometers is based on conversion of the spectral-domain signal into the time domain by a dispersive element. A photonic crystal fiber is used as a dispersive fiber in this spectrometer. Resolution enhancement of considered spectrometer with photonic crystal fibers is studied. The fiber loss and length minimization in this spectrometer are obtained. Simulation results show that an increase in resolution will cause a decrease in the speed of the spectrometer. Trade offs between resolution and speed are studied.     

Design and simulation of nano-antenna with tunable direction of radiation

A new concept for tuning direction of radiation in nano-antenna with negative and positive polarizability is introduced and evaluated by numerical simulation. It has shown that such a configuration consisted of an array of nanoparticles can tune the direction of radiation pattern successfully. Increasing the number of nanoparticles exhibits more directivity at special direction. Variation in position of nanoparticles is used to tune the angle of maximum radiation. The maximum tunability for special geometry of three particles is about 40°. Angle of maximum radiation pattern for four particles is shifted from interval 150–180° to 0–30°.     

Experimental and Modelling Study of Gravity Drainage in a Three-Block System

Transport in Porous Media - Gravity drainage is known as the controlling mechanism of oil recovery in naturally fractured reservoirs. The efficiency of this mechanism is controlled by...     

Real solutions of the direct geometrico-static problem of under-constrained cable-driven parallel robots with 3 cables: a numerical investigation

This paper addresses the direct geometrico-static problem of under-constrained cable-driven parallel robots with 3 cables. The task at hand consists in finding all equilibrium configurations of the end-effector when the cable lengths are assigned. This problem is known to admit 156 solutions in the complex field, but the upper bound on the number of real solutions is as yet an open issue. Finding this bound is the objective of the paper. For this purpose, three numerical approaches are developed, namely a continuation procedure adapted from an algorithm originally proposed by Dietmaier and two evolutionary techniques based on a genetic algorithm and particle swarm optimization. In all cases, a number of sets of robot parameters for which the direct geometrico-static problem provides at the most 54 real configurations is found. The coherence of the obtained results leads to conjecture that the achieved bound is tight. However, formal proof is yet to be discovered.     

On the Thermal Degradation of a Novel Epoxy-Based Nanocomposite Cured With Tryptophan as an Environment-Friendly Curing Agent

The thermal degradation kinetics of diglycidyl ether of bisphenol-A (DGEBA) cured with tryptophan (Trp) in the presence of silica nanoparticles (SiNP) was investigated by thermogravimetry analysis. The activation energies of the solid-state decomposition process were evaluated using the advanced isoconversional method. The dependence of conversion (degradation) on the temperature and activation energy was determined allowing the calculation of master plots. The experimental master plots agreed with the first-order (F1) kinetic function for both neat epoxy and nanocomposite in the conversion range of 0.45–0.85. Using the kinetic model and the calculated kinetic parameters, the times at half conversion (α = 0.5) were computed for different degradation temperatures. The kinetic analysis showed that the degradation rate of the epoxy nanocomposite was lower than that of the neat epoxy for conversions between 0.45 and 0.85. Therefore, we concluded that adding SiNP to DGEBA/Trp can improve the thermal stability in the conversion range of 0.45–0.85.     

Peculiarities of magnetic flux penetration and trapping in monocrystalline (YBa2Cu3O7−x and Bi2Sr2CaCu2O8+x ) and polycrystalline (YBa2Cu3O7−x ) samples

A comparative analysis of the magnetic flux trapping in monocrystalline and polycrystalline HTSC samples is carried out, and the possibility of employing the dependence of the trapped magnetic flux on the external magnetic field for obtaining comparative estimates of the effect of pinning centers is determined.