Exploring the Utility of Compound-Specific Isotope Analysis for Assessing Ferrous Iron-Mediated Reduction of RDX in the Subsurface

Abstract

This work determines isotope enrichment factors for RDX during reduction by iron minerals. The values determined are used to assess the transformation of RDX at a site with groundwater contamination and in laboratory column reactors simulating in situ chemical reduction treatment.

Abiotic reduction of 3-nitro-1,2,4-triazol-5-one (NTO) and other munitions constituents by wood-derived biochar through its rechargeable electron storage capacity

“…DNAN and RDX were detected at 4.8 and 3.4 min, respectively, and quantified based on absorbance at 214 nm. The same method was used to quantify daughter products of DNAN 48 and RDX: 49,50 2ANAN, 4ANAN, and DAAN were measured at 4.2 min (254 nm), 3.2 min (234 nm) and 2.3 min (210 nm), respectively, and MNX, DNX, and TNX were detected at 2.9, 2.4, and 1.9 min, respectively, based on absorbance at 234 nm. Nitrite, a potential RDX reduction product, 51 was measured using Hach NitriVer® 3 Nitrite Reagent (Loveland, CO) and a Vernier LabQuest 2 UV-vis spectrophotometer.…”

Anisotropic oxidative growth of goethite-coated sand particles in column reactors during 4-chloronitrobenzene reduction by Fe(ii)/goethite

“…[6][7][8][9][10][11][12][13][14][15] The reduction of many common groundwater contaminants (e.g., nitroaromatic compounds, NACs), which are commonly used in munitions, insecticides, herbicides, and pharmaceuticals, as well as feedstocks for industrial chemicals [16][17][18][19][20][21][22] are well studied. 23,24 The release of nitro munitions, such as 2,4,6trinitrotoluene (TNT), and hexahydro-1,3, 5-trinitro-1,3,5-triazine (RDX) and their associated by-or/and degradation products into the aquatic environment through load, assembly, and packing processes has put many munition plants under the National Priorities List for Superfund cleanup. 25 The continuous regeneration of surface-bound Fe(II) with low standard reduction potential (EH 0 ) via microbial iron-reduction 11 and generation of reducing capacity by in situ redox manipulation 26,27 produce conditions suitable for reducing oxidized contaminants, such as NACs, in soils, sediments and aquifers.…”

Machine Learning to Predict Effective Reaction Rates in 3D Porous Media From Pore Structural Features

“…This is because reactive transport in porous media is a strongly coupled process involving complex fluid flow, solute transport, and chemical reactions. Indeed, significant discrepancies between reaction rates measured from well-mixed reactors and effective reaction rates measured from column experiments and field observations have been reported [18][19][20][21] .…”