New Trends in Separation Techniques of Lithium Isotopes: A Review of Chemical Separation Methods

Purification of Quinoline Insolubles in Heavy Coal Tar and Preparation of Meso-Carbon Microbeads by Catalytic Polycondensation

The quinoline-insoluble (QI) matter in coal tar and coal tar pitch is an important factor affecting the properties of subsequent carbon materials. In this paper, catalytic polycondensation was used to remove QI from heavy coal tar, and meso-carbon microbeads could be formed during the purification process. The results showed that AlCl3 had superior catalytic performance to CuCl2, and the content of QI and heavy components, including pitch, in the coal tar was lower after AlCl3 catalytic polycondensation. Under the condition of catalytic polycondensation (AlCl3 0.9 g, temperature 200 °C, and time 9 h), AlCl3 could reduce the QI content in heavy coal tar. The formed small particles could be filtered and removed, and good carbon materials could be obtained under the condition of catalytic polycondensation (AlCl3 0.9 g, temperature 260 °C, and time 3 h).

Preparation and Performance of Ferric-Rich Bauxite-Tailing-Based Thermal Storage Ceramics

In recent years, regenerative thermal oxidizer (RTO) has been widely used in the petroleum industry, chemical industry, etc. The massive storage required by solid waste has become a serious problem. Due to their chemical composition, bauxite tailings as raw materials for high-temperature thermal storage ceramics show enormous potential in the fields of research and application. In this study, we propose a method for preparing ferric-rich and high specific storage capacity by adding Fe2O3 powder to bauxite tailings. Based on a 7:3 mass ratio of bauxite tailings to lepidolite, Fe2O3 powder with different mass fractions (7 wt%, 15 wt%, 20 wt%, 30 wt%, and 40 wt%) was added to the ceramic material to improve the physical properties and thermal storage capacity of thermal storage ceramics. The results showed that ferric-rich thermal storage ceramics with optimal performance were obtained by holding them at a sintering temperature of 1000 °C for 2 h. When the Fe2O3 content was 15 wt%, the bulk density of the thermal storage ceramic reached 2.53 g/cm3, the compressive strength was 120.81 MPa, and the specific heat capacity was 1.06 J/(g·K). This study has practical guidance significance in the preparation of high thermal storage ceramics at low temperatures and low costs.

Assessing dead time effects when attempting isotope ratio quantification by time-of-flight secondary ion mass spectrometry

Time-of-flight secondary ion mass spectrometry (TOF-SIMS) is a quasi-non-destructive technique capable of analyzing the outer monolayers of a solid sample and detecting all elements of the periodic table and their isotopes. Its ability to analyze the outer monolayers resides in sputtering the sample surface with a low-dose primary ion gun, which, in turn, imposes the use of a detector capable of counting a single ion at a time. Consequently, the detector saturates when more than one ion arrives at the same time hindering the use of TOF-SIMS for quantification purposes such as isotope ratio estimation. Even though a simple Poisson-based correction is usually implemented in TOF-SIMS acquisition software to compensate the detector saturation effects, this correction is only valid up to a certain extent and can be unnoticed by the inexperienced user. This tutorial describes a methodology based on different practices reported in the literature for dealing with the detector saturation effects and assessing the validity limits of Poisson-based correction when attempting to use TOF-SIMS data for quantification purposes. As a practical example, a dried lithium hydroxide solution was analyzed by TOF-SIMS with the aim of estimating the 6Li/7Li isotope ratio. The approach presented here can be used by new TOF-SIMS users on their own data for understanding the effects of detector saturation, determine the validity limits of Poisson-based correction, and take into account important considerations when treating the data for quantification purposes.