Ultrathin and Smooth Pheomelanin-like Photoconductive Film.

This study introduces a facile method for the substrate-independent deposition of pheomelanin-like films, revealing unique and promising electrical characteristics. The conventional darkening of a dopamine solution at a basic pH was significantly delayed by the addition of l-cysteine, resulting in a distinctive temporal pattern: an initial quiescent period without apparent color change followed by an abrupt and explosive burst. Surprisingly, within the quiescent period, the deposition of ultrathin and smooth pheomelanin-like films was observed, in addition to rough and thick films formed after the burst. Regardless of thickness or texture, these films exhibited common chemical properties, including moisture-capturing capability and dark- and bright-state conductivities. Particularly noteworthy were consistent photocurrent responses under bias voltage across various pheomelanin-like films, which were not observed in polydopamine films, highlighting the influential role of l-cysteine addition. These findings present a novel avenue for the potential application of pheomelanin-like films in bioelectronics, emphasizing their distinct electrical characteristics and prompting further exploration into their intricate conductive mechanisms. The study contributes to advancing our understanding of melanin-based materials and their potential in diverse scientific and technological domains.

Deep Learning-Based Inkjet Droplet Detection for Jetting Characterizations and Multijet Synchronization.

Inkjet printing is a powerful direct material writing process. It can be used to deposit microfluidic droplets in designated patterns at submicrometer resolution, which reduces materials usage. Nonetheless, predicting jetting characterizations is not easy because of the intrinsic complexity of the ink-nozzle-air interactions. Thus, inkjet processes are monitored by skilled engineers to ensure process reliability. This is a bottleneck in industry, resulting in high labor costs for multiple nozzles. To address this, we present a deep learning-based method for jetting characterizations. Inkjet printing is recorded by an in situ CCD camera and each droplet is detected by YOLOv5, a 1-stage detector using a convolutional neural network (CNN). The precision, recall, and mean average precision (mAP) at a 0.5 intersection over the union (IoU) threshold of the trained model were 0.86, 0.89, and 0.90, respectively. Each regression result for a detected droplet is accumulated in chronological order for each class of droplet and nozzle. The quantified information includes velocity, diameter, length, and translation, which can be used to synchronize multinozzle jetting and, eventually, the printed patterns. This demonstrates the feasibility of autonomous real-time process testing for large-scale electronics manufacturing, such as the high-resolution patterning of biosensor electrodes and QD display pixels while exploiting big data obtained from jetting characterizations.

Multi-Feature Automatic Extraction for Detecting Obstructive Sleep Apnea Based on Single-Lead Electrocardiography Signals.

Obstructive sleep apnea (OSA), a prevalent sleep disorder, is intimately associated with various other diseases, particularly cardiovascular conditions. The conventional diagnostic method, nocturnal polysomnography (PSG), despite its widespread use, faces challenges due to its high cost and prolonged duration. Recent developments in electrocardiogram-based diagnostic techniques have opened new avenues for addressing these challenges, although they often require a deep understanding of feature engineering. In this study, we introduce an innovative method for OSA classification that combines a composite deep convolutional neural network model with a multimodal strategy for automatic feature extraction. This approach involves transforming the original dataset into scalogram images that reflect heart rate variability attributes and Gramian angular field matrix images that reveal temporal characteristics, aiming to enhance the diversity and richness of data features. The model comprises automatic feature extraction and feature enhancement components and has been trained and validated on the PhysioNet Apnea-ECG database. The experimental results demonstrate the model's exceptional performance in diagnosing OSA, achieving an accuracy of 96.37%, a sensitivity of 94.67%, a specificity of 97.44%, and an AUC of 0.96. These outcomes underscore the potential of our proposed model as an efficient, accurate, and convenient tool for OSA diagnosis.

Measurement of inkjet droplet size based on Fraunhofer diffraction.

Inkjet printers have started to manufacture OLED/QLED pixel arrays for the display industry, and the precise measurement and control of ink droplet volume during the printing process has become important. We investigated the feasibility of Fraunhofer diffraction analysis as a volume measurement tool for fast-moving inkjet droplets. To confirm the basic idea, two Fraunhofer diffraction-based methods were used to calculate the wire diameters of well-known sized and steady-positioned metal wires. The first method was to curve-fit the whole measured diffraction intensity curve with the extensive Fraunhofer diffraction equation. The second one was to use the simple approximate diameter calculation equation with the measured position data of minimum diffraction intensity. The metal wire diameters calculated by the two methods showed less than 1.17% error. For the size measurement of fast-moving inkjet droplets, the first method showed 24.5 µm diameter and 7.7 pL volume, while the second method showed 25.4 µm diameter and 8.58 pL volume. We found that the second method was more suitable for real-time inkjet monitoring because its average computer calculation time was 33 ms, and the first method took an average of 34 ms, about 1000 times more CPU time. Hence, Fraunhofer diffraction analysis as an inkjet droplet volume measurement tool was feasible with a good balance of measurement time and measurement accuracy.

Fe-Catalyzed C-H Alkenylation of Dialkyl Anilines with Disulfonylethenes.

The oxidative alkenylation reaction of α-aminoalkyl C(sp3)-H bonds has been investigated with (E)-1,2-bis(sulfonyl)ethenes. The catalytic process of iron-polypyridyl complexes drives the single-electron oxidation of dialkyl anilines, resulting in the formation of α-aminoalkyl radical species. Subsequent cascades of radical addition and elimination reactions ensue, ultimately leading to the generation of sulfonylated allylic amine products. The utility of these products extends further, enabling the synthesis of multisubstituted heterocycles like pyrroles, pyrazines, and triazoles.

Catechol-Amyloid Interactions.

This review introduces multifaceted mutual interactions between molecules containing a catechol moiety and aggregation-prone proteins. The complex relationships between these two molecular species have previously been elucidated primarily in a unidirectional manner, as demonstrated in cases involving the development of catechol-based inhibitors for amyloid aggregation and the elucidation of the role of functional amyloid fibers in melanin biosynthesis. This review aims to consolidate scattered clues pertaining to catechol-based amyloid inhibitors, functional amyloid scaffold of melanin biosynthesis, and chemically designed peptide fibers for providing chemical insights into the role of the local three-dimensional orientation of functional groups in manifesting such interactions. These orientations may play crucial, yet undiscovered, roles in various supramolecular structures.

Amyloid Fibers Increase Free Radicals of Synthetic Melanin.

Functional amyloid fibers are crucial in melanogenesis, but their roles are incompletely understood. In particular, their relationship with intrinsic spin characters of melanin remains unexplored. Here, we show that adding an amyloid scaffold greatly augments the spin density in synthetic melanin. It also brings about concurrent alterations in water dispersibility, bandgaps, and radical scavenging properties of the synthetic melanin, which facilitates its applications in solar water remediation and protection of human keratinocytes from UV irradiation. This work provides implications in the unrevealed role of functional amyloid in melanogenesis and in the origin of the superiority of natural melanin toward its synthetic variants in terms of the spin-related properties.

Rod-to-sphere elemental reconstruction of biocompatible Ag2Te-Ag4.53-Te3 nanoparticles for triple negative breast cancer photo-nano-therapy.

Silver nanoparticles (AgNPs) continue to be applied to agricultural and medical applications because of their antibacterial and antifungal effects. However, AgNPs are vulnerable to poisoning by oxidation or sulfidation, and unintentional toxicity can occur via leaching. Therefore, ensuring the stability of AgNPs for practical applications is considered an important requirement. In this study, we propose the solvothermal galvanic replacement of a Te nanorod (TeNR) template with a Ag precursor to manufacture highly stable and biocompatible Ag-Te nanoparticles (AgTeNPs). In addition to their high stability, AgTeNPs composed of Ag2Te-Ag4.53Te3 were evaluated as a nanotherapeutic agent enabled by their selective toxicity through metabolic degradation in breast cancer cells. It has been demonstrated that combinatorial treatment with hyperthermic cancer-cell ablation through photothermal conversion provides an effective cancer treatment in vitro and in vivo. The discovered new biocompatible Ag nanomaterials with innate anticancer effects are expected to be applied to various application fields.

Iron Catalysis of C(sp3)-H Azidation Using a Heteroarene Radical Cation Strategy.

The FeIII(phen)3 catalysis of the benzylic C(sp3)-H azidation of indoles has been investigated. The Fe(III) complex can selectively oxidize indoles to form arene radical cations, which are transformed into benzylic C(sp3) radical intermediates. This strategy exhibits a difference in reactivity between N-heteroarenes and benzene, which is difficult to achieve via direct hydrogen abstraction approaches. Various biorelevant azide precursors were constructed, highlighting the utility of this mild first-row transition-metal catalyst system.

The Effect of Pre-Stretched Substrate on the Electrical Resistance of Printed Ag Nanowires.

One-dimensional nanomaterials have drawn attention as an alternative electrode material for stretchable electronics. In particular, silver nanowires (Ag NWs) have been studied as stretchable electrodes for strain sensors, 3D electronics, and freeform-shaped electronic circuits. In this study, Ag NWs ink was printed on the pre-stretched silicone rubber film up to 40% in length using a drop-on-demand dispenser. After printing, silicone rubber film was released and stretched up to 20% as a cyclic test with 10-time repetition, and the ratios of the resistance of the stretched state to that of the released state (Rstretched/Rreleased) were measured at each cycle. For Ag NWs electrode printed on the pre-stretched silicone rubber at 30%, Rstretched/Rreleased at 10% and 20% strain was 1.05, and 1.57, respectively, which is significantly less than about 7 for Ag NWs at the 10% strain without pre-stretched substrate. In the case of 10% strain on the 30% pre-stretched substrate, the substrate is stretched and the contact points with Ag NWs were not changed much as the silicone rubber film stretched, which meant that Ag NWs may slide between other Ag NWs. Ag NWs electrode on the 40% pre-stretched substrate was stretched, strain was concentrated on the Ag NWs electrode and failure of electrode occurred, because cracks occurred at the surface of silicone rubber film when it was pre-stretched to 40%. We confirmed that printed Ag NWs on the pre-stretched film showed more contact points and less electric resistance compared to printed Ag NWs on the film without pre-stretching.

Randomly Disassembled Nanostructure for Wide Angle Light Extraction of Top-Emitting Quantum Dot Light-Emitting Diodes.

The quantum dot light-emitting diode (QLED) represents one of the strongest display technologies and has unique advantages like a shallow emission spectrum and superior performance based on the cumulative studies of state-of-the-art quantum dot (QD) synthesis and interfacial engineering. However, research on managing the device's light extraction has been lacking compared to the conventional LED field. Moreover, relevant studies on top-emitting QLEDs (TE-QLEDs) have been severely lacking compared to bottom-emitting QLEDs (BE-QLEDs). This paper demonstrates a novel light extraction structure called the randomly disassembled nanostructure (RaDiNa). The RaDiNa is formed by detaching polydimethylsiloxane (PDMS) film from a ZnO nanorod (ZnO NR) layer and laying it on top of the TE-QLED. The RaDiNa-attached TE-QLED shows significantly widened angular-dependent electroluminescence (EL) intensities over the pristine TE-QLED, confirming the effective light extraction capability of the RaDiNa layer. Consequently, the optimized RaDiNa-attached TE-QLED achieves enhanced external quantum efficiency (EQE) over the reference device by 60%. For systematic analyses, current-voltage-luminance (J-V-L) characteristics are investigated using scanning electron microscopy (SEM) and optical simulation based on COMSOL Multiphysics. It is believed that this study's results provide essential information for the commercialization of TE-QLEDs.

Surface-facilitated formation of polydopamine and its implications in melanogenesis.

This manuscript examines influences of differently functionalized surfaces on the formation of solution-dispersed polydopamine (pDA). Glass vials functionalized with different functional groups provided a set of conditions with which the relationship between the area of active surface and the rate of pDA formation could be systematically studied. The results suggest that charged and polar surfaces accelerate pDA formation in solution, with the effect of -NH2 surfaces being exceptionally strong. In the vials, pDA formed as both forms of dispersions in solution and films at solid-liquid interface. Further analyses confirmed that both forms of pDA formed with -NH2 surfaces were chemically similar to conventional pDA synthesized without help of functional surfaces. Among short peptide-based amyloid fibers with defined surface functional groups, and those displaying lysines (-NH2) greatly accelerated the formation of pDA, consistent with the results of -NH2-functionalized vials. The results suggest that pDA formation may be facilitated by surface functional groups of solid-liquid interfaces, and have implications for the overlooked roles of amyloid fibers in biological melanogenesis.

Designed Amyloid Fibers with Emergent Melanosomal Functions.

Short peptides designed to self-associate into amyloid fibers with metal ion-binding ability have been used to catalyze various types of chemical reactions. This manuscript demonstrates that one of these short-peptide fibers coordinated with CuII can exhibit melanosomal functions. The coordinated CuII and the amyloid structure itself are differentially functional in accelerating oxidative self-association of dopamine into melanin-like species and in regulating their material properties (e.g., water dispersion, morphology, and the density of unpaired electrons). The results have implications for the role of functional amyloids in melanin biosynthesis and for designing peptide-based supramolecular structures with various emergent functions.

A temporal dependency feature in lower dimension for lung sound signal classification.

Respiratory sounds are expressed as nonlinear and nonstationary signals, whose unpredictability makes it difficult to extract significant features for classification. Static cepstral coefficients such as Mel-frequency cepstral coefficients (MFCCs), have been used for classification of lung sound signals. However, they are modeled in high-dimensional hyperspectral space, and also lose temporal dependency information. Therefore, we propose shifted [Formula: see text]-cepstral coefficients in lower-subspace (SDC-L) as a novel feature for lung sound classification. It preserves temporal dependency information of multiple frames nearby same to original SDC, and improves feature extraction by reducing the hyperspectral dimension. We modified EMD algorithm by adding a stopping rule to objectively select a finite number of intrinsic mode functions (IMFs). The performances of SDC-L were evaluated with three machine learning techniques (support vector machine (SVM), k-nearest neighbor (k-NN) and random forest (RF)) and two deep learning algorithms (multilayer perceptron (MLP) and convolutional neural network (cNN)) and one hybrid deep learning algorithm combining cNN with long short term memory (LSTM) in terms of accuracy, precision, recall and F1-score. We found that the first 2 IMFs were enough to construct our feature. SVM, MLP and a hybrid deep learning algorithm (cNN plus LSTM) outperformed with SDC-L, and the other classifiers achieved equivalent results with all features. Our findings show that SDC-L is a promising feature for the classification of lung sound signals.

Task Migration and Scheduler for Mixed-Criticality Systems.

The interference between software components is increasing in safety-critical domains, such as autonomous driving. Low-criticality (LC) tasks, such as vehicle communication, may control high-criticality (HC) tasks, such as acceleration. In such cases, the LC task should also be considered as an HC task because the HC tasks relies on the LC task. However, the difficulty in guaranteeing these LC tasks is the catastrophic cost of computing resources, the electronic control unit in the domain of vehicles, required for every task. In this paper, we theoretically and practically provide safety-guaranteed and inexpensive scheduling for LC tasks by borrowing the computational power of neighbored systems in distributed systems, obviating the need for additional hardware components. As a result, our approach extended the schedulability of LC tasks without violating the HC tasks. Based on the deadline test, the compatibility of our approach with the task-level MC scheduler was higher than that of the system-level MC scheduler, such that the task-level had all dropped LC tasks recovered while the system-level only had 25.5% recovery. Conversely, from the worst-case measurement of violated HC tasks, the HC tasks were violated by the task-level MC scheduler more often than by the system-level MC scheduler, with 70.3% and 15.4% average response time overhead, respectively. In conclusion, under the condition that the HC task ratio has lower than 47% of the overall task systems at 80% of total utilization, the task-level approach with task migration has extensively higher sustainability on LC tasks.

Deep-Learning-Based Microfluidic Droplet Classification for Multijet Monitoring.

Inkjet printing, the deposition of microfluidic droplets on a specified area, has gained increasing attention from both academia and industry for its versatility and scalability for mass production. Inkjet printing productivity depends on the number of nozzles used in a multijet process. However, droplet jetting conditions can vary for each nozzle due to multiple factors, such as the surface wetting condition of the nozzle, properties of the ink, and variances in the manufacturing of the nozzle head. For these reasons, droplet jetting conditions must be continuously monitored and evaluated by skillful engineers. The present study presents a deep-learning-based method to identify the droplet jetting status of a single-jet printing process. A convolutional neural network (CNN)-based on the MobileNetV2 model was employed with optimized hyperparameters to classify the inkjet frames containing images captured with a CCD camera. By accumulating the classified class data in order by frame time, the jetting conditions could be evaluated with high accuracy. The method was also successfully demonstrated with a multijet process, with a test time of less than a second per image.

Nucleophilic Regulation of the Formation of Melanin-like Species by Amyloid Fibers.

This work examines the influences of amyloid fibers of hen egg white lysozyme (HEWL) on the formation of melanin-like species (MLS) with a rationally selected set of catechol derivatives. Catechol-amyloid interactions, which are central in melanogenesis, are complex and multifaceted, making them difficult to understand at the molecular level. The catechol derivatives are set to interact with HEWL amyloid fibers upon altering pH, and the resultant formation of MLS is characterized. For obtaining clues for the molecular mechanism by which HEWL fibers regulate the formation of MLS, putative intermolecular interactions are individually perturbed and their ramifications are analyzed. With the entire data set, we could conclude that the externally presented nucleophilic moieties of HEWL fibers play a major role in regulating the material and kinetic properties of MLS and their formation, respectively.

Facile fabrication of flexible metal grid transparent electrode using inkjet-printed dot array as sacrificial layer.

In this study, we introduce a flexible metal grid transparent electrode fabricated using a lift-off process. This transparent electrode consisting of metal thin film with punched-like pattern by hole array was fabricated with 8 um separations. The separation of inkjet-printed etching resistant ink droplets was controlled in order to investigate the relationship between its electrical and optical properties of the electrodes. The aluminum areal density was defined to predict the electrical and optical properties of different arrays. A high and uniform transmittance spectrum appears to extend broadly into the UV region. The figure of merit of the transparent electrode was investigated in order to determine its performance as a transparent electrode. Moreover, there was no significant change in the resistance after 7000 bending cycles, indicating that the array conductor had superior stability. We also demonstrate transparent touch screen panels fabricated using the transparent electrode.

Residual-Solvent-Induced Morphological Transformation by Intense Pulsed Light on Spin-Coated and Inkjet-Printed ZnO NP Films for Quantum-Dot Light-Emitting Diodes.

It was demonstrated through a comparison between the spin-coated and inkjet-printed quantum-dot light-emitting diodes' (QLED) performance analysis outcomes that the annealing temperature of a zinc oxide nanoparticle (ZnO NP) electron transport layer (ETL) optimized for intense pulsed light (IPL) via a post-treatment differs depending on the film-formation method used. For a naturally dried ZnO NP ETL formulated without annealing, different film morphologies were observed according to the film-formation method of spin coating and inkjet printing, and the surface-roughness root mean square (RMS) value was increased in an IPL post-treatment due to unevaporated residual solvent. Based on this phenomenon, we classified and analyzed different film profiles according to the deposition method, the presence or absence of annealing, and the annealing temperature.

Neuronal Migration on Silicon Microcone Arrays with Different Pitches.

Neuronal migration is a complicated but fundamental process for proper construction and functioning of neural circuits in the brain. Many in vivo studies have suggested the involvement of environmental physical features of a neuron in its migration, but little effort has been made for the in vitro demonstration of topography-driven neuronal migration. This work investigates migratory behaviors of primary hippocampal neurons on a silicon microcone (SiMC) array that presents 14 different pitch domains (pitch: 2.5-7.3 µm). Neuronal migration becomes the maximum at the pitch of around 3 µm, with an upper migration threshold of about 4 µm. Immunocytochemical studies indicate that the speed and direction of migration, as well as its probability of occurrence, are correlated with the morphology of the neuron, which is dictated by the pitch and shape of underlying SiMC structures. In addition to the effects on neuronal migration, the real-time imaging of migrating neurons on the topographical substrate reveals new in vitro modes of neuronal migration, which have not been observed on the conventional flat culture plate, but been suggested by in vivo studies.