An electrospun nanofiber membrane significantly improves the electrical performances of triboelectric nanogenerators (TENGs) due to its high surface area. In recent years, composite nanofibers were applied to a TENG using various electrospinning system types to further enhance the performance of TENGs; however, the effects of the systems on the energy harvesting capability of TENGs have not been investigated thoroughly. This study aims to fabricate polyimide/poly(vinylidene fluoride-co-trifluoroethylene) composite nanofiber-based TENGs with three different nozzle systems: single nozzle, conjugated nozzle, and multinozzles, and two different collectors: plate collector and drum collector. A TENG with multinozzle-drum system-based nanofibers produced an output voltage of 364 V, a short-circuit current of 17.2 μA, a transferred charge of 29.72 nC, and a power density of 2.56 W/m2 at a load resistance of 100 MΩ, which were ∼7 times higher than those of other system-based nanofibers. Under the 10,000 cycles of loading, the TENG stably harvested electric energy. The TENG could also harvest energy from the human body motions, and it is sufficient to illuminate 117 light-emitting diodes and drive several electronic devices. The proposed TENG exhibits excellent electric performances as a wearable energy harvester.
In this study, we report an approach to achieve sequence-specific counting of single DNA molecules, which is required for more versatile applications of the previously reported absolute DNA quantification technique based on flow cytometric DNA single molecule counting. While using the same capillary-based flow cytometric setup, fluorescence activation of a target DNA was made with a number of fluorescent oligonucleotide probes of complementary sequences to that of a target DNA. The feasibility of the proposed approach was tested with 7 kb single-strand M13 DNA as the target DNA for sequence specific counting for quantification. Sample preparation, the number of fluorescent oligonucleotide probes, and hybridization conditions mainly matter for the performance of the proposed method. Using a set of 30 sequence-specific fluorescent probes with a selected hybridization buffer, acceptable performance was confirmed through comparison with other conventional methods such as digital polymerase chain reaction (dPCR), UV spectrophotometry, and deoxyribonucleoside monophosphate analysis by mass spectrometry. Proven comparability to the dPCR method confirmed the feasibility of the proposed approach. With further improvement in instrumentation, the proposed method is expected to become established as a reference measurement procedure for sequence-specific quantification of nucleic acids working under a uniquely straightforward measurement principle.
The development of pressure sensors with high sensitivity and effectiveness that exhibit linearity over a wide pressure range is crucial for wearable devices. In this study, we fabricated a novel ionic liquid (IL)/polymer composite with a convex and randomly wrinkled microstructure in a cost-effective and facile manner using an opaque glass and stretched polydimethylsiloxane template. The fabricated IL/polymer composite was used as the dielectric layer in a capacitive pressure sensor. The sensor exhibited a high linear sensitivity of 56.91 kPa–1 owing to the high interfacial capacitance formed by the electrical double layer of the IL/polymer composite over a relatively wide range (0–80 kPa). We also demonstrated the sensor performance for various applications such as a glove-attached sensor, sensor array, respiration monitoring mask, human pulse, blood pressure measurement, human motion detection, and a wide range of pressure sensing. It would be expected that the proposed pressure sensor has sufficient potential for use in wearable devices.
Motivation Protein–ligand binding affinity prediction is a central task in drug design and development. Cross-modal attention mechanism has recently become a core component of many deep learning models due to its potential to improve model explainability. Non-covalent interactions (NCIs), one of the most critical domain knowledge in binding affinity prediction task, should be incorporated into protein–ligand attention mechanism for more explainable deep drug–target interaction models. We propose ArkDTA, a novel deep neural architecture for explainable binding affinity prediction guided by NCIs. Results Experimental results show that ArkDTA achieves predictive performance comparable to current state-of-the-art models while significantly improving model explainability. Qualitative investigation into our novel attention mechanism reveals that ArkDTA can identify potential regions for NCIs between candidate drug compounds and target proteins, as well as guiding internal operations of the model in a more interpretable and domain-aware manner. Availability ArkDTA is available at https://github.com/dmis-lab/ArkDTA Contact kangj@korea.ac.kr
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