Charge-Localized Retention and Long-Term Memory Enabled by Cooperating Sterically Confined Molecular Crystallization with Spiro[fluorene-9,9'-xanthene]-Based Csp3-Hindrance.

Charge localization of memory materials plays a crucial role in the endurance and retention ability of organic nonvolatile memory, which is completely opposite from the charge delocalization of high-mobility materials. However, charge transfer of both though-space and through-bond based on molecular design principles still faces challenges. Herein, a nonplanar wide-bandgap semiconductor with Csp3-hindrance (DOCH3-DDPA-SFX) has been designed and synthesized. An effective crystallization effect of self-assembled two-dimensional nanosheets on charge trapping dynamics and kinetics is visualized by Kelvin probe force microscopy (KPFM). The trapped charges are localized completely on a single nanosheet, which has better charge trapping and retention properties than an amorphous film. Meanwhile, crystallization also greatly improves structure stability. Combining DFT theoretical calculations, the mechanisms of localization and long-term retention are discussed. The steric crystallization effects on the charge localization will guide the effective design of single-component semiconducting charge-memory materials by molecular assembly and aggregate control for high-performance organic memory.

MRI-Based Kinetic Heterogeneity Evaluation in the Accurate Access of Axillary Lymph Node Status in Breast Cancer Using a Hybrid CNN-RNN Model.

BACKGROUND: Accurate evaluation of the axillary lymph node (ALN) status is needed for determining the treatment protocol for breast cancer (BC). The value of magnetic resonance imaging (MRI)-based tumor heterogeneity in assessing ALN metastasis in BC is unclear. PURPOSE: To assess the value of deep learning (DL)-derived kinetic heterogeneity parameters based on BC dynamic contrast-enhanced (DCE)-MRI to infer the ALN status. STUDY TYPE: Retrospective. SUBJECTS: 1256/539/153/115 patients in the training cohort, internal validation cohort, and external validation cohorts I and II, respectively. FIELD STRENGTH/SEQUENCE: 1.5 T/3.0 T, non-contrast T1-weighted spin-echo sequence imaging (T1WI), DCE-T1WI, and diffusion-weighted imaging. ASSESSMENT: Clinical pathological and MRI semantic features were obtained by reviewing histopathology and MRI reports. The segmentation of the tumor lesion on the first phase of T1WI DCE-MRI images was applied to other phases after registration. A DL architecture termed convolutional recurrent neural network (ConvRNN) was developed to generate the KHimage (kinetic heterogeneity of DCE-MRI image) score that indicated the ALN status in patients with BC. The model was trained and optimized on training and internal validation cohorts, tested on two external validation cohorts. We compared ConvRNN model with other 10 models and the subgroup analyses of tumor size, magnetic field strength, and molecular subtype were also evaluated. STATISTICAL TESTS: Chi-squared, Fisher's exact, Student's t, Mann-Whitney U tests, and receiver operating characteristics (ROC) analysis were performed. P < 0.05 was considered significant. RESULTS: The ConvRNN model achieved area under the curve (AUC) of 0.802 in the internal validation cohort and 0.785-0.806 in the external validation cohorts. The ConvRNN model could well evaluate the ALN status of the four molecular subtypes (AUC = 0.685-0.868). The patients with larger tumor sizes (>5 cm) were more susceptible to ALN metastasis with KHimage scores of 0.527-0.827. DATA CONCLUSION: A ConvRNN model outperformed traditional models for determining the ALN status in patients with BC. LEVEL OF EVIDENCE: 3 TECHNICAL EFFICACY: Stage 2.

Fe-doping and oxygen vacancy achieved by electrochemical activation and precipitation/dissolution equilibrium in NiOOH for oxygen evolution reaction.

The poor conductivities and instabilities of accessible nickel oxyhydroxides hinder their use as oxygen evolution reaction (OER) electrocatalysts. Herein, we constructed Fe-NiOOH-OV-600, an Fe-doped nickel oxide hydroxide with abundant oxygen vacancies supported on nickel foam (NF), using a hydrothermal method and an electrochemical activation strategy involving 600 cycles of cyclic voltammetry, assisted by the precipitation/dissolution equilibrium of ferrous sulfide (FeS) in the electrolyte. This two-step method endows the catalyst with abundant Fe-containing active sites while maintaining the ordered structure of nickel oxide hydroxide (NiOOH). Characterization and density functional theory (DFT) calculations revealed that synergy between trace amounts of the Fe dopant and the oxygen vacancies not only promotes the generation of reconstructed active layers but also optimizes the electronic structure and adsorption capacity of the active sites. Consequently, the as-prepared Fe-NiOOH-OV-600 delivered large current densities of 100 and 1000 mA cm-2 for the OER at overpotentials of only 253 and 333 mV in 1 mol/L KOH. Moreover, the catalyst is stable for at least 100 h at 500 mA cm-2. This work provides insight into the design of efficient transition-metal-based electrocatalysts for the OER.