Self‐emulsion polymerization of amphiphilic monomers—a green route to synthesis of polymeric nanoscaffolds

Self‐emulsion polymerization (SEP), a green route developed by us for the polymerization of amphiphilic monomers, does not require any emulsifier or an organic solvent except that the water‐soluble initiators such as 2,2′‐azobis[2‐(2‐imidazolin‐2‐yl)propane]dihydrochloride (VA‐044) and potassium persulfate (KPS) are only used. We report here the polymer nanoscaffolds from a number of amphiphilic monomers, which can be used for in situ encapsulation of a variety of nanoparticles. As a demonstration of the efficacy of these nanoscaffolds, the synthesis of a biocompatible hybrid nanoparticle (nanohybrid), prepared by encapsulating Fe₃O₄ magnetic nanoparticle (Fe₃O₄ MNPs) in poly(2‐hydroxyethyl methacrylate) in water, for MRI application is presented. The nanohybrid prepared following the SEP in the form of an emulsion does not involve the use of any stabilizing agent, crosslinker, polymeric emulsifier, or surfactant. This water‐soluble, spherical, and stable nanohybrid containing Fe₃O₄ MNPs of average size 10 ± 2 nm has a zeta potential value of ?41.89 mV under physiological conditions. Magnetic measurement confirmed that the nanohybrid shows typical magnetic behavior having a saturation magnetization (Ms) value of 32.3 emu/g and a transverse relaxivity (r2) value of 29.97 mM^?1 s^?1, which signifies that it can be used as a T2 contrast agent in MRI. © 2019 Wiley Periodicals, Inc. J. Polym. Sci., Part A: Polym. Chem. 2019     

On $$\epsilon $$ ϵ -solutions for robust semi-infinite optimization problems

Positivity - In this paper, we consider semi-infinite optimization problems involving a convex objective function and infinitely many convex constraint functions with data uncertainty, and give its...     

Simultaneous determination of 75 abuse drugs including amphetamines,benzodiazepines, cocaine,opioids, piperazines,zolpidem and metabolites in human hair samples using liquid chromatography–tandem mass spectrometry

A liquid chromatography–tandem mass spectrometric method for the simultaneous determination of 75 abuse drugs and metabolites, including 19 benzodiazepines, 19 amphetamines, two opiates, eight opioids, cocaine, lysergic acid diethylamide, zolpidem, three piperazines and 21 metabolites in human hair samples, was developed and validated. Ten‐milligram hair samples were decontaminated, pulverized using a ball mill, extracted with 1 mL of methanol spiked with 28 deuterated internal standards in an ultrasonic bath for 60 min at 50°C, and purified with Q‐sep dispersive solid‐phase extraction tubes. The purified extracts were evaporated to dryness and the residue was dissolved in 0.1 mL of 10% methanol. The 75 analytes were analyzed on an Acquity HSS T3 column using gradient elution of methanol and 0.1% formic acid and quantified in multiple reaction monitoring mode with positive electrospray ionization. Calibration curves were linear (r ≥ 0.9951) from the lower limit of quantitation (2–200 pg/mg depending on the drug) to 2000 pg/mg. The coefficients of variation and accuracy for intra‐ and inter‐assay analysis at three QC levels were 4.3–12.9% and 89.2–109.1%, respectively. The overall mean recovery ranged from 87.1 to 105.3%. This method was successfully applied to the analysis of 11 forensic hair samples obtained from drug abusers.     

MAO‐free and extremely active catalytic system for ethylene tetramerization

The original Sasol catalytic system for ethylene tetramerization is composed of a Cr source, a PNP ligand, and MAO (methylaluminoxane). The use of expensive MAO in excess has been a critical concern in commercial operation. Many efforts have been made to replace MAO with non‐coordinating anions (e.g., [B(C₆F₅)₄]^?); however, most of such attempts were unsuccessful. Herein, an extremely active catalytic system that avoids the use of MAO is presented. The successive addition of two equivalent [H(OEt₂)₂]⁺[B(C₆F₅)₄]^? and one equivalent CrCl₃(THF)₃ to (acac)AlEt₂ and subsequent treatment with a PNP ligand [CH₃(CH₂)₁₆]₂C(H)N(PPh₂)₂ ( 1 ) yielded a complex presumably formulated as [ 1 ‐CrAl (acac)Cl₃(THF)]²⁺[B(C₆F₅)₄]^?₂, which exhibited high activity when combined with iBu₃Al (1120 kg/g‐Cr/h; ~4 times that of the original Sasol system composed of Cr (acac)₃, iPrN(PPh₂)₂, and MAO). Via the introduction of bulky trialkylsilyl substituents such as –SiMe₃, –Si(nBu)₃, or –SiMe₂(CH₂)₇CH₃ at the para‐position of phenyl groups in 1 (i.e., by using [CH₃(CH₂)₁₆]₂C(H)N[P(C₆H₄‐p‐SiR₃)₂]₂ instead of 1 ), the activities were dramatically improved, i.e., tripled (2960–3340 kg/g‐Cr/h; more than 10 times that of the original Sasol system). The generation of significantly less PE (<0.2 wt%) even at a high temperature is another advantage achieved by the introduction of bulky trialkylsilyl substituents. NMR studies and DFT calculations suggest that increase of the steric bulkiness on the alkyl‐N and P‐aryl moieties restrict the free rotation around (alkyl)N–P (aryl) bonds, which may cause the generation of more robust active species in higher proportion, leading to extremely high activity along with the generation of a smaller amount of PE.     

Strategies Towards Capturing Nitrogenase Substrates and Intermediates via Controlled Alteration of Electron Fluxes

Nitrogenase utilizes an ATP-dependent reductase to deliver electrons to its catalytic component to enable two important reactions: the reduction of N₂ to NH₄⁺, and the reduction of CO to hydrocarbons. The two nitrogenase-based reactions parallel the industrial Haber–Bosch and Fischer–Tropsch processes, yet they occur under ambient conditions. As such, understanding the enzymatic mechanism of nitrogenase is crucial for the future development of biomimetic strategies for energy-efficient production of valuable chemical commodities. Mechanistic investigations of nitrogenase has long been hampered by the difficulty to trap substrates and intermediates relevant to the nitrogenase reactions. Recently, we have successfully captured CO on the Azotobacter vinelandii V-nitrogenase via two approaches that alter the electron fluxes in a controlled manner: one approach utilizes an artificial electron donor to trap CO on the catalytic component of V-nitrogenase in the resting state; whereas the other employs a mismatched reductase component to reduce the electron flux through the system and consequently accumulate CO on the catalytic component of V-nitrogenase. Here we summarize the major outcome of these recent studies, which not only clarified the catalytic relevance of the one-CO (lo-CO) and multi-CO (hi-CO) bound states of nitrogenase, but also pointed to a potential competition between N₂ and CO for binding to the same pair of reactive Fe sites across the sulfur belt of the cofactor. Together, these results highlight the utility of these strategies in poising the cofactor at a well-defined state for substrate- or intermediate-trapping via controlled alteration of electron fluxes, which could prove beneficial for further elucidation of the mechanistic details of nitrogenase-catalyzed reactions.     

Competitive hydrogen bonding of cyclopropenium ions: Implications of an anion switch

This work demonstrates how modulating hydrogen bonding between intermolecular, bifurcated, and intramolecular interactions can be used to tune the structural, electronic, and photophysical properties of cyclopropenium ions and their respective fluorophores. The basis of this switchability was examined using X-ray diffraction analysis, ¹H NMR spectroscopy, DFT calculations, and fluorescence spectroscopy.     

Predicting Quantum Many-Body Dynamics with Transferable Neural Net works

Advanced machine learning(ML)approaches such as transfer learning have seldom been applied to approximate quantum many-body systems.Here we demonstrate that a simple recurrent unit(SRU)based efficient and transferable sequence learning framework is capable of learning and accurately predicting the time evolution of the one-dimensional(ID)Ising model with simultaneous transverse and parallel magnetic fields,as quantitatively corroborated by relative entropy measurements between the predicted and exact state distributions.At a cost of constant computational complexity,a larger many-body state evolution is predicted in an autoregressive way from just one initial state,without any guidance or knowledge of any Hamiltonian.Our work paves the way for future applications of advanced ML methods in quantum many-body dynamics with knowledge only from a smaller system.