A Transparent Hydrogel-Ionic Conductor with High Water Retention and Self-Healing Ability.

This study presents a transparent and ion-conductive hydrogel with suppressed water loss. The hydrogel comprises agarose polymer doped with sucrose and sodium chloride salt (NaCl-Suc/A hydrogel). Sucrose increases the water retention of the agarose gel, and the Na and Cl ions dissolved in the gel provide ionic conductivity. The NaCl-Suc/A gel shows high retention capability and maintains a 45% water uptake after 4 h of drying at 60 °C without encapsulation at the optimum gel composition. The doped NaCl-Suc/A hydrogel demonstrates improved mechanical properties and ionic conductivity of 1.6 × 10-2 (S/cm) compared to the pristine agarose hydrogel. The self-healing property of the gel restores the electrical continuity when reassembled after cutting. Finally, to demonstrate a potential application of the ion-conductive hydrogel, a transparent and flexible pressure sensor is fabricated using the NaCl-Suc/A hydrogel, and its performance is demonstrated. The results of this study could contribute to solving problems with hydrogel-based devices such as rapid dehydration and poor mechanical properties.

Dielectrophoretic Capture of Cancer-Derived Small-Extracellular-Vesicle-Bound Janus Nanoparticles via Lectin-Glycan Interaction.

Glycosylation is closely related to cellular metabolism and disease progression. In particular, glycan levels in cancer cells and tissues increase during cancer progression. This upregulation of glycosylation in cancer cells may provide a basis for the development of new biomarkers for the targeting and diagnosis of specific cancers. Here, they developed a detection technology for pancreatic cancer cell-derived small extracellular vesicles (PC-sEVs) based on lectin-glycan interactions. Lectins specific for sialic acids are conjugated to Janus nanoparticles to induce interactions with PC-sEVs in a dielectrophoretic (DEP) system. PC-sEVs are selectively bound to the lectin-conjugated Janus nanoparticles (lectin-JNPs) with an affinity comparable to that of conventionally used carbohydrate antigen 19-9 (CA19-9) antibodies. Furthermore, sEVs-bound Lectin-JNPs (sEVs-Lec-JNPs) are manipulated between two electrodes to which an AC signal is applied for DEP capture. In addition, the proposed DEP system can be used to trap the sEVs-Lec-JNP on the electrodes. Their results, which are confirmed by lectin-JNPs using the proposed DEP system followed by target gene analysis, provide a basis for the development of a new early diagnostic marker based on the glycan characteristics of PC-sEVs. In turn, these novel detection methods could overcome the shortcomings of commercially available pancreatic cancer detection techniques.

The Dielectrophoretic Alignment of Biphasic Metal Fillers for Thermal Interface Materials.

Pad-type thermal interface materials (TIMs) with composite structures are required to exhibit high thermal conductivity while maintaining conformal contact with the heat sink, which is strongly influenced by the type and content of the thermally conductive filler. This study presents that biphasic metal particles can be effectively aligned using the dielectrophoretic chaining (DEP-C) mechanism, thereby enhancing the thermal conductivity of a pad-type TIM. A eutectic gallium-indium (EGaIn) alloy liquid metal and solid copper were used as the filler materials with two different phases. The biphasic metal particle mixture of EGaIn and Cu (EGaIn-Cu) were better aligned by DEP-C than when they presented individually because fusion between the two particles increased the effective size. As expected, the thermal conductivity of the TIM composites increased when DEP-C aligned the filler. Notably, TIMs with both EGaIn-Cu fillers showed the largest increase in thermal conductivity, of up to 64.6%, and the highest thermal conductivity values after DEP-C application compared to TIMs with only the EGaIn or Cu filler. Finally, the heat dissipation performance of the TIM composite on a lit light-emitting diode is shown, where the TIM with DEP-C-aligned fillers exhibits improved performance.

Accurate Visual Simultaneous Localization and Mapping (SLAM) against Around View Monitor (AVM) Distortion Error Using Weighted Generalized Iterative Closest Point (GICP).

Accurately estimating the pose of a vehicle is important for autonomous parking. The study of around view monitor (AVM)-based visual Simultaneous Localization and Mapping (SLAM) has gained attention due to its affordability, commercial availability, and suitability for parking scenarios characterized by rapid rotations and back-and-forth movements of the vehicle. In real-world environments, however, the performance of AVM-based visual SLAM is degraded by AVM distortion errors resulting from an inaccurate camera calibration. Therefore, this paper presents an AVM-based visual SLAM for autonomous parking which is robust against AVM distortion errors. A deep learning network is employed to assign weights to parking line features based on the extent of the AVM distortion error. To obtain training data while minimizing human effort, three-dimensional (3D) Light Detection and Ranging (LiDAR) data and official parking lot guidelines are utilized. The output of the trained network model is incorporated into weighted Generalized Iterative Closest Point (GICP) for vehicle localization under distortion error conditions. The experimental results demonstrate that the proposed method reduces localization errors by an average of 39% compared with previous AVM-based visual SLAM approaches.