BPF: a novel cluster boundary points detection method for static and streaming data

Data points situated near a cluster boundary are called boundary points and they can represent useful information about the process generating this data. The existing methods of boundary points detection cannot differentiate boundary points from outliers as they are affected by the presence of outliers as well as by the size and density of clusters in the dataset. Also, they require tuning of one or more parameters and prior knowledge of the number of outliers in the dataset for tuning. In this research, a boundary points detection method called BPF is proposed which can effectively differentiate boundary points from outliers and core points. BPF combines the well-known outlier detection method Local Outlier Factor (LOF) with Gravity value to calculate the BPF score. Our proposed algorithm StaticBPF can detect the top-m boundary points in the given dataset. Importantly, StaticBPF requires tuning of only one parameter i.e. the number of nearest neighbors \((k)\) and can employ the same \(k\) used by LOF for outlier detection. This paper also extends BPF for streaming data and proposes StreamBPF. StreamBPF employs a grid structure for improving k-nearest neighbor computation and an incremental method of calculating BPF scores of a subset of data points in a sliding window over data streams. In evaluation, the accuracy of StaticBPF and the runtime efficiency of StreamBPF are evaluated on synthetic and real data where they generally performed better than their competitors.

     

Atomic Tuning in Electrically Conducting Bimetallic Organic Frameworks for Controllable Electromagnetic Wave Absorption

Due to the structural complexity and limited controllability of conventional microwave-absorption materials (MAMs), the precise regulation of atomically-resolved structures and properties of MAMs remains a significant challenge. The interpretation of the dielectric loss mechanism is usually conduction and polarization losses, while the absence of components-regulated template materials further hinders the disclosure of the mechanism. Herein, based on the customizable functionality of the MOF platform, a series of pristine bimetallic MOFs with precise and controllable conductivity are successfully constructed through the bimetallic alloying strategy. The controllability is attributed to the alteration of free-carrier concentration and the subtle difference of interlayer displacement or spacing, both of which originate from the atomic tuning of hetero-metal. Notably, Cu_1.3Ni_1.7(HITP)₂ features an ultra-high absorption strength (reflection loss, RL = −71.5 dB) and an ultra-wide maximum bandwidth (6.16 GHz) at only 15% filling, which ranked in the upper range of all reported MAMs. Due to the metal co-synergies, Cu_1.3Ni_1.7(HITP)₂ possesses a switchable high absorption peak in a wide range (4–18 GHz). This work opens up an avenue for tailoring the atomic tuning of new MOF-based electromagnetic wave absorbers and provides a conceptually novel platform.     

Screening chloride Li-ion conductors using high-throughput force-field molecular dynamics

The realization of high-energy-density all-solid-state Li-ion batteries requires materials exhibiting both high Li-ion conductivity and high deformability, as exemplified by Li₃MCl₆-type chlorides. Herein, we optimized the classical force-field (FF) parameters for 36 Li- and Cl-containing compounds to reproduce the results of high-precision first-principles calculations and performed rapid FF molecular dynamics (MD) calculations to determine their Li-ion conductivities. In addition, shear moduli were evaluated by first-principles calculations and used as a deformability index. Li₄Mn₃Cl₁₀ was selected based on its Li-ion conductivity, stiffness, and thermodynamic stability. In accordance with the low calculated shear modulus (11.7 GPa), the cold-pressed compact had a high relative density of 98%, which indicated good deformability. The room-temperature conductivity (3.9 mS cm⁻¹) was similar to that (1.6 mS cm⁻¹) obtained by high-precision first-principles MD calculations. The Li-ion conductivity of synthesized Li₄Mn₃Cl₁₀ (18 µS cm⁻¹) was relatively rather high compared to those of known chloride materials but much lower than the calculated value, which was ascribed to the fact that calculations were performed for the high-temperature phase, whereas synthesis yielded the low-temperature phase. The material screening method greatly increases the speed of material exploration and expands the application possibilities of chloride materials for all-solid-state batteries.     

Mapping Nanomechanical Properties of Basal Surfaces in Metastatic Intestinal 3D Living Organoids with High-Speed Scanning Ion Conductance Microscopy

Studying mechanobiology is increasing of scientific interests in life science and nanotechnology since its impact on cell activities (e.g., adhesion, migration), physiology, and pathology. The role of apical surface (AS) and basal surface (BS) of cells played in mechanobiology is significant. The mechanical mapping and analysis of cells mainly focus on AS while little is known about BS. Here, high-speed scanning ion conductance microscope as a powerful tool is utilized to simultaneously reveal morphologies and local elastic modulus (E) of BS of genotype-defined metastatic intestinal organoids. A simple method is developed to prepare organoid samples allowing for long-term BS imaging. The multiple nano/microstructures, i.e., ridge-like, stress-fiber, and E distributions on BS are dynamically revealed. The statistic E analysis shows softness of BS derived from eight types of organoids following a ranking: malignant tumor cells > benign tumor cells > normal cells. Moreover, the correlation factor between morphology and E is demonstrated depending on cell types. This work as first example reveals the subcellular morphologies and E distributions of BS of cells. The results would provide a clue for correlating genotype of 3D cells to malignant phenotype reflected by E and offering a promising strategy for early-stage diagnosis of cancer.