Remediation of Dichloromethane (CH₂Cl₂) Using Non-thermal, Atmospheric Pressure Plasma Generated in a Packed-Bed Reactor

This work describes the application of a non-thermal plasma generated in a dielectric barrier packed-bed plasma reactor for the remediation of dichloromethane (CH₂Cl₂, DCM). The overall aim of this investigation is to identify the role of key process parameters and chemical mechanisms on the removal efficiency of DCM in plasma. The influence of process parameters, such as oxygen concentration, concentration of initial volatile organic compounds (VOCs), energy density, plasma residence time, and background gas, on the removal efficiency of 500 ppm DCM was investigated. Results showed a maximum removal efficiency with the addition of 2–4% oxygen into a nitrogen plasma. It is thought that oxygen concentrations in excess of 4% decreased the decomposition of chlorinated VOCs as a result of ozone and nitrogen oxide formation. Increasing the residence time and the energy density resulted in increasing the removal efficiency of chlorinated VOCs in plasma. A chemical kinetic model has been developed on the basis of the proposed reaction scheme, and the calculation of end product concentrations are in general good agreement with the observed values. With the understanding of the effect of the key parameters, it has been possible to optimize the remediation process

Changes in subplot basal area.

<p>Changes in basal area for plots that (a) declined in basal area by >25% or (b) showed declines of <25% or increased in basal area. Solid black lines represent predictions from linear mixed models, with grey area representing 95% confidence intervals for these predictions. Points represent individual subplots and grey lines represent the trajectory of these subplots.</p

Relationship between subplot stem density and total subplot basal area.

<p>Points represent individual plots in 1964 (red circles), 1996 (green triangles) and 2014 (blue squares). The solid line represents the prediction from a mixed model of this relationship with the grey band representing the coefficient 95% confidence intervals. Note that both the x and y axes are log transformed.</p

Relationships between juvenile tree density and canopy openness.

<p>Relationships between density of beech (a, c) seedlings and (b, d) saplings and canopy openness in woodlands in the New Forest showing signs of dieback. Graphs a and b use data from Denny Wood while graphs c and d use data collected from the gradient sites (12 sites). Solid lines represent predictions from coefficients with P ≤ 0.05 and grey bands represent 95% confidence intervals of these predictions.</p

Additional file 1: of Measuring multiple parameters of CD8+ tumor-infiltrating lymphocytes in human cancers by image analysis

Table S1 Relevant image information for nonclinical sample sets. Table S2 Nonclinical tumor samples, key patient data.Table S3 Overview of data used for statistical analysis. Table S4 Validation of CD8 IA using CD8 single stain and CD8/PD-L1 dual stain. (DOCX 91 kb

Additional file 2: of Measuring multiple parameters of CD8+ tumor-infiltrating lymphocytes in human cancers by image analysis

CD8/PD-L1 dual IHC, quality control; co-registration; details of the automated classification assessment; details of digital IA scoring solutions; measurement of CD8+ TILs in PD-L1–positive tumor. (DOCX 45 kb

Additional file 3: of Measuring multiple parameters of CD8+ tumor-infiltrating lymphocytes in human cancers by image analysis

Figure S1 IA classification of CD8+ lymphocytes. Figure S2 IA classification of elongate CD8+ lymphocytes. Figure S3 Elongate CD8+ TILS detected by IA are also CD3+. Figure S4 Tumor landscape of elongate CD8+ TILs. (DOCX 1795 kb