Vol 50, No 11 (2019)

Functionalized 2,5-dithienylthiazolo[5,4-d]thiazole (DTTzTz) derivatives have attracted interest towards application as non-fullerene acceptors in solution-processed organic solar cells. Here, we present a combined high-field electron paramagnetic resonance and density functional theory study of the light-induced negative polaron on the novel acceptor 2,4-diCN-Ph-DTTzTz formed after charge transfer in bulk heterojunction blends with a donor polymer. Despite spectral overlap with the polymer cation, the g-anisotropy of the acceptor radical could be directly confirmed through detection of its unique ¹⁴N hyperfine couplings using electron–electron double resonance (ELDOR)-detected nuclear magnetic resonance (EDNMR) for spectral filtering. The spectral assignment is further underpinned by quantum-chemical calculations, which also provide detailed information about the spin density and charge distribution of the polaron in the DTTzTz acceptor.

An estimated 3.3 million people are living with a traumatic brain injury (TBI)-associated morbidity. Currently, only invasive and sacrificial methods exist to study neurochemical alterations following TBI. Nuclear magnetic resonance methods—magnetic resonance imaging (MRI) and spectroscopy (MRS)—are powerful tools which may be used noninvasively to diagnose a range of medical issues. These methods can be utilized to explore brain functionality, connectivity, and biochemistry. Unfortunately, many of the commonly studied brain metabolites (e.g., N-acetyl-aspartate, choline, creatine) remain relatively stable following mild to moderate TBI and may not be suitable for longitudinal assessment of injury severity and location. Therefore, a critical need exists to investigate alternative biomarkers of TBI, such as acrolein. Acrolein is a byproduct of lipid peroxidation and accumulates following damage to neuronal tissue. Acrolein has been shown to increase in post-mortem rat brain tissue following TBI. However, no methods exist to noninvasively quantify acrolein in vivo. Currently, we have characterized the T₁ and T₂ of acrolein via nuclear magnetic resonance saturation recovery and Carr–Purcell–Meiboom–Gill experiments, accordingly, to maximize the signal-to-noise ratio of acrolein obtained with MRS. In addition, we have quantified acrolein in water and whole-brain phantom using PRESS MRS and standard post-processing methods. With this potential novel biomarker for assessing TBI, we can investigate methods for predicting acute and chronic neurological dysfunction in humans and animal models. By quantifying and localizing acrolein with MRS, and investigating neurological outcomes associated with in vivo measures, patient-specific interventions could be developed to decrease TBI-associated morbidity and improve quality of life.

Occasionally in proton high-resolution magic angle spinning nuclear magnetic resonance spectroscopy (¹H HR-MAS NMR), the standard field shimming across the region-of-interest with the active shims is not sufficient in the presence of substantial magnetic susceptibility gradients. This can be ascribed to the presence of large air pocket within the sample, the proximity between sample and probehead (especially with micro-sized probes), or data acquisition at high magnetic fields. Herein, the study demonstrates a simple approach to enhance the capacity of the shim fields—by supplementing a specific passive ferro-shim—for unifying the field distributions across the sample. Qualitative field numerical analyses were carried out to illustrate the effectiveness of the applied passive shims together with the active shims.

Magnetic resonance imaging (MRI) frequently requires transform domain de-noising methods to preserve important features in the reconstructed images such as corners, sharp structures, and edges. Wavelet transform-based image de-noising is a standard approach used in MRI to recover smooth surface and sharp edges from the given noisy MR images, thereby improving diagnostic interpretations. Parallel magnetic resonance imaging (pMRI) techniques such as SENSE have been recently developed with an aim to improve the data acquisition speed, signal-to-noise ratio (SNR), and spatial resolution of the reconstructed images. However, the SENSE reconstruction algorithm often encounters noise during data acquisition and reconstruction process which not only contaminates the quality of the reconstructed images but also leads to poor diagnostic interpretations in clinical settings. During SENSE reconstruction process, noise can appear in the reconstructed image mainly due to two reasons (1) imperfections in the receiver coils; (2) un-folding the aliased images of multiple receiver coils to obtain a single composite image. In this paper, a new adaptive patch-based filtering in wavelet domain is presented to recover sharp structures and edges without introducing any artifacts in the SENSE reconstructed images. The proposed method uses soft-thresholding function as a shrinkage process which typically involves thresholding the small wavelet coefficients to reduce the noise without affecting the important features in the SENSE reconstructed images. For the evaluation of the proposed method, several experiments are performed using simulated phantom and in vivo data sets. The SENSE reconstruction quality using the proposed method is compared with contemporary de-nosing techniques, in terms of structural similarity index (SSIM) and peak signal-to-noise ratio (PSNR). Experimental results demonstrate that the SENSE reconstruction using the proposed method when compared to the other contemporary de-nosing methods successfully removes the noise and preserves the fine details in the reconstructed MR images without introducing blurring artifacts.