Use of cetyltrimethylammonium bromide as surfactant for the determination of copper and chromium in gasoline emulsions by electrothermal atomic absorption spectrometry

In this work, the use of cetyltrimethylammonium bromide as surfactant for the preparation of oil-in-water emulsions for the determination of Cu and Cr in gasoline by electrothermal atomic absorption spectrometry (ET AAS) was evaluated. The surfactant amount was tested in the range of 25 to 300 mg, added to 2 ml of gasoline, and completed to 10 mL with 0.1% (v/v) nitric acid solution. 150 mg of surfactant was found optimum, and a sonication time of 10 min sufficient to form an oil-in-water emulsion that was stable for several hours. The ET AAS temperature program was established based on pyrolysis and atomization curves. The pyrolysis temperatures were set at 700 and 1300 °C for Cu and Cr, respectively and the selected atomization temperatures were 2400 and 2500 °C. The time and temperature of the drying stage and the atomization time were experimentally tested to provide optimum conditions. The limits of detection were found to be 5 μg L^− 1 and 1.5 μg L^− 1 for Cu and Cr, respectively in the original gasoline samples. The relative standard deviation (RSD) ranged from 4 to 9% in oil-in-water emulsions spiked with 5 μg L^− 1 and 15 μg L^− 1 of each metal, respectively. Recoveries varied from 90 to 98%. The accuracy of the proposed method was tested by an alternate procedure using complete evaporation of the gasoline sample. The method was adequate for the determination of Cu and Cr in gasoline samples collected from different gas stations in Salvador, BA, Brazil.     

Beyond generative models: superfast traversal,optimization, novelty,exploration and discovery (STONED) algorithm for molecules using SELFIES

Inverse design allows the generation of molecules with desirable physical quantities using property optimization. Deep generative models have recently been applied to tackle inverse design, as they possess the ability to optimize molecular properties directly through structure modification using gradients. While the ability to carry out direct property optimizations is promising, the use of generative deep learning models to solve practical problems requires large amounts of data and is very time-consuming. In this work, we propose STONED – a simple and efficient algorithm to perform interpolation and exploration in the chemical space, comparable to deep generative models. STONED bypasses the need for large amounts of data and training times by using string modifications in the SELFIES molecular representation. First, we achieve non-trivial performance on typical benchmarks for generative models without any training. Additionally, we demonstrate applications in high-throughput virtual screening for the design of drugs, photovoltaics, and the construction of chemical paths, allowing for both property and structure-based interpolation in the chemical space. Overall, we anticipate our results to be a stepping stone for developing more sophisticated inverse design models and benchmarking tools, ultimately helping generative models achieve wider adoption.

Interpolation and exploration within the chemical space for inverse design.     

Particle and surface characterization of a natural illite and study of its copper retention

Illite clays are known to have a strong affinity for metallic pollutants in the environment and can be applied as low-cost adsorbents for industrial waste treatment. A crucial factor in the development of such applications, however, is the understanding of the chemical, mineralogical, and colloidal properties of these clays. It is also important to understand the mechanisms involved in the surface adsorption of metals by these adsorbants. In order to study the retention of transition metals on illite clays, we have applied surface characterization techniques such as FPIA, SEM-EDX, XRD, N2 (77 K) adsorption, and FTIR. In addition to these experimental techniques, we have also employed a theoretical model that accounts for the chemistry of transition metal ions, and considers the global retention process to be the sum of several single retention processes. This model adequately fits the experimental data and allows for the speciation of metal retention on illite surfaces. Between pH values of 2.53 and 3.01 the only adsorption processes are the electrostatic sorption of [Cu(H2O)6]2+, and the surface complexation of [Cu(H2O)6]2+ and [Cu(OH)(H2O)5]+ ions. Surface complexation of [Cu(OH)(H2O)5]+ ions increases with pH, overcoming [Cu(H2O)6]2+ retention, and thus contributing to the surface precipitation of Cu(OH)2.     

A Simple Method for Producing Pure (+) trans-1,2-Epoxylimonene

In this paper we report a simple methodology for obtaining (+) trans - 1,2 - epoxylimonene (1b) in high purity (>99% HRGC) and reasonable quantity. The mixture of 1a and 1b in CH₂Cl₂ is stirred with 1M NaHSO₃ in water. Under these conditions 1a is completely destroyed, while 1b is only partially destroyed.     

Influence of heat treatment temperature of carbon fiber felt substrate on polyaniline electrosynthesis and its properties

Journal of Solid State Electrochemistry - Polyaniline was electrosynthesized using three voltammetric cycles on carbon fiber felts annealed at 1400, 1600, 2000, and 2300 K. Felts and...     

Hybrid sol–gel silica adsorbent materials synthesized by molecular imprinting for tannin removal

Journal of Sol-Gel Science and Technology - This study reports the development of a functional adsorbent synthesized by the molecular imprinting method in a sol–gel matrix. The adsorption...     

A Carbocationic Triarylmethane-Based Porous Covalent Organic Network

A thermally stable carbocationic covalent organic network (CON), named RIO-70 was prepared from pararosaniline hydrochloride, an inexpensive dye, and triformylphloroglucinol in solvothermal conditions. This nanoporous organic material has shown a specific surface area of 990 m² g⁻¹ and pore size of 10.3 Å. The material has CO₂ uptake of 2.14 mmol g⁻¹ (0.5 bar), 2.7 mmol g⁻¹ (1 bar), and 6.8 mmol g⁻¹ (20 bar), the latter corresponding to 3 CO₂ molecules adsorbed per pore per sheet. It is shown to be a semiconductor, with electrical conductivity (σ) of 3.17×10⁻⁷ S cm⁻¹, which increases to 5.26×10⁻⁴ S cm⁻¹ upon exposure to I₂ vapor. DFT calculations using periodic conditions support the findings.     

Analysis of high resolution electron spin resonance spectra

Previous interpretations of the electron spin resonance spectrum of Wurster's Blue cation are shown to be incorrect. In particular the nitrogen hyperfine splitting constant previously quoted as 0·28 gauss is, in fact, 7·0 gauss. A complete analysis of the spectrum is described.