Halide Perovskites and Their Derivatives for Efficient, High-Resolution Direct Radiation Detection: Design Strategies and Applications.

The past decade has witnessed a rapid rise in the performance of optoelectronic devices based on lead-halide perovskites (LHPs). The large mobility-lifetime products and defect tolerance of these materials, essential for optoelectronics, also make them well-suited for radiation detectors, especially given the heavy elements present, which is essential for strong X-ray and γ-ray attenuation. Over the past decade, LHP thick films, wafers, and single crystals have given rise to direct radiation detectors that have outperformed incumbent technologies in terms of sensitivity (reported values up to 3.5 × 106 µC Gyair -1 cm-2 ), limit of detection (directly measured values down to 1.5 nGyair s-1 ), along with competitive energy and imaging resolution at room temperature. At the same time, lead-free perovskite-inspired materials (e.g., methylammonium bismuth iodide), which have underperformed in solar cells, have recently matched and, in some areas (e.g., in polarization stability), surpassed the performance of LHP detectors. These advances open up opportunities to achieve devices for safer medical imaging, as well as more effective non-invasive analysis for security, nuclear safety, or product inspection applications. Herein, the principles behind the rapid rises in performance of LHP and perovskite-inspired material detectors, and how their properties and performance link with critical applications in non-invasive diagnostics are discussed. The key strategies to engineer the performance of these materials, and the important challenges to overcome to commercialize these new technologies are also discussed.

Polarimetric imaging combining optical parameters for classification of mice non-melanoma skin cancer tissue using machine learning.

Polarimetric imaging systems combining machine learning is emerging as a promising tool for the support of diagnosis and intervention decision-making processes in cancer detection/staging. A present study proposes a novel method based on Mueller matrix imaging combining optical parameters and machine learning models for classifying the progression of skin cancer based on the identification of three different types of mice skin tissues: healthy, papilloma, and squamous cell carcinoma. Three different machine learning algorithms (K-Nearest Neighbors, Decision Tree, and Support Vector Machine (SVM)) are used to construct a classification model using a dataset consisting of Mueller matrix images and optical properties extracted from the tissue samples. The experimental results show that the SVM model is robust to discriminate among three classes in the training stage and achieves an accuracy of 94 % on the testing dataset. Overall, it is provided that polarimetric imaging systems and machine learning algorithms can dynamically combine for the reliable diagnosis of skin cancer.

Characterization of Mueller matrix elements for classifying human skin cancer utilizing random forest algorithm.

SIGNIFICANCE: The Mueller matrix decomposition method is widely used for the analysis of biological samples. However, its presumed sequential appearance of the basic optical effects (e.g., dichroism, retardance, and depolarization) limits its accuracy and application. AIM: An approach is proposed for detecting and classifying human melanoma and non-melanoma skin cancer lesions based on the characteristics of the Mueller matrix elements and a random forest (RF) algorithm. APPROACH: In the proposal technique, 669 data points corresponding to the 16 elements of the Mueller matrices obtained from 32 tissue samples with squamous cell carcinoma (SCC), basal cell carcinoma (BCC), melanoma, and normal features are input into an RF classifier as predictors. RESULTS: The results show that the proposed model yields an average precision of 93%. Furthermore, the classification results show that for biological tissues, the circular polarization properties (i.e., elements m44, m34, m24, and m14 of the Mueller matrix) dominate the linear polarization properties (i.e., elements m13, m31, m22, and m41 of the Mueller matrix) in determining the classification outcome of the trained classifier. CONCLUSIONS: Overall, our study provides a simple, accurate, and cost-effective solution for developing a technique for classification and diagnosis of human skin cancer.

Nanostructured mesophase electrode materials: modulating charge-storage behavior by thermal treatment.

3D nanostructured carbonaceous electrode materials with tunable capacitive phases were successfully developed using graphene/particulate polypyrrole (PPy) nanohybrid (GPNH) precursors without a separate process for incorporating heterogeneous species. The electrode material, namely carbonized GPNHs (CGPNHs) featured a mesophase capacitance consisting of both electric double-layer (EDL) capacitive and pseudocapacitive elements at the molecular level. The ratio of EDL capacitive element to pseudocapacitive element (E-to-P) in the mesophase electrode materials was controlled by varying the PPy-to-graphite weight (Pw/Gw) ratio and by heat treatment (TH), which was demonstrated by characterizing the CGPNHs with elemental analysis, cyclic voltammetry, and a charge/discharge test. The concept of the E-to-P ratio (EPR) index was first proposed to easily identify the capacitive characteristics of the mesophase electrode using a numerical algorithm, which was reasonably consistent with the experimental findings. Finally, the CGPNHs were integrated into symmetric two-electrode capacitor cells, which rendered excellent energy and power densities in both aqueous and ionic liquid electrolytes. It is anticipated that our approach could be widely extended to fabricating versatile hybrid electrode materials with estimation of their capacitive characteristics.

Detailed electrochemical analysis of the redox chemistry of tetrafluorotetracyanoquinodimethane TCNQF4, the radical anion [TCNQF4]•-, and the dianion [TCNQF4]2- in the presence of trifluoroacetic acid.

The electrochemistry of 2,3,5,6-tetrafluoro-7,7,8,8-tetracyanoquinodimethane (TCNQF(4)), [TCNQF(4)](•-), and [TCNQF(4)](2-) have been studied in acetonitrile (0.1 M [Bu(4)N][ClO(4)]). Transient and steady-state voltammetric techniques have been utilized to monitor the generation of [TCNQF(4)](•-) and [TCNQF(4)](2-) anions as well as their reactions with trifluoroacetic acid (TFA). In the absence of TFA, the reduction of TCNQF(4) occurs via two, diffusion controlled, chemically and electrochemically reversible, one-electron processes where the reversible formal potentials are 0.31 and -0.22 V vs Ag/Ag(+). Unlike the TCNQ analogues, both [TCNQF(4)](•-) and [TCNQF(4)](2-) are persistent when generated via bulk electrolysis even under aerobic conditions. Voltammetric and UV-vis data revealed that although the parent TCNQF(4) does not react with TFA, the electrochemically generated radical anion and dianion undergo facile protonation to yield [HTCNQF(4)](•), [HTCNQF(4)](-) and H(2)TCNQF(4) respectively. The voltammetry can be simulated to give a complete thermodynamic and kinetic description of the complex, coupled redox and acid-base chemistry. The data indicate dramatically different equilibrium and rate constants for the protonation of [TCNQF(4)](•-) (K(eq) = 3.9 × 10(-6), k(f) = 1.0 × 10(-3) M(-1) s(-1)) and [TCNQF(4)](2-) (K(eq) = 3.0 × 10(3), k(f) = 1.0 × 10(10) M(-1) s(-1)) in the presence of TFA.