Nanoribbon Yarn with Versatile Inorganic Materials.

Nanomaterial-based yarns have been actively developed owing to their advantageous features, namely, high surface-area-to-volume ratios, flexibility, and unusual material characteristics such as anisotropy in electrical/thermal conductivity. The superior properties of the nanomaterials can be directly imparted and scaled-up to macro-sized structures. However, most nanomaterial-based yarns have thus far, been fabricated with only organic materials such as polymers, graphene, and carbon nanotubes. This paper presents a novel fabrication method for fully inorganic nanoribbon yarn, expanding its applicability by bundling highly aligned and suspended nanoribbons made from various inorganic materials (e.g., Au, Pd, Ni, Al, Pt, WO3, SnO2, NiO, In2O3, and CuO). The process involves depositing the target inorganic material on a nanoline mold, followed by suspension through plasma etching of the nanoline mold, and twisting using a custom-built yarning machine. Nanoribbon yarn structures of various functional inorganic materials are utilized for chemical sensors (Pd-based H2 and metal oxides (MOx)-based green gas sensors) and green energy transducers (water splitting electrodes/triboelectric nanogenerators). This method is expected to provide a comprehensive fabrication strategy for versatile inorganic nanomaterials-based yarns.

Micro-/Nanohierarchical Structures Physically Engineered on Surfaces: Analysis and Perspective.

The high demand for micro-/nanohierarchical structures as components of functional substrates, bioinspired devices, energy-related electronics, and chemical/physical transducers has inspired their in-depth studies and active development of the related fabrication techniques. In particular, significant progress has been achieved in hierarchical structures physically engineered on surfaces, which offer the advantages of wide-range material compatibility, design diversity, and mechanical stability, and numerous unique structures with important niche applications have been developed. This review categorizes the basic components of hierarchical structures physically engineered on surfaces according to function/shape and comprehensively summarizes the related advances, focusing on the fabrication strategies, ways of combining basic components, potential applications, and future research directions. Moreover, the physicochemical properties of hierarchical structures physically engineered on surfaces are compared based on the function of their basic components, which may help to avoid the bottlenecks of conventional single-scale functional substrates. Thus, the present work is expected to provide a useful reference for scientists working on multicomponent functional substrates and inspire further research in this field.

Highly Sensitive Soft Pressure Sensors for Wearable Applications Based on Composite Films with Curved 3D Carbon Nanotube Structures.

Soft pressure sensors based on 3D microstructures exhibit high sensitivity in the low-pressure range, which is crucial for various wearable and soft touch applications. However, it is still a challenge to manufacture soft pressure sensors with sufficient sensitivity under small mechanical stimuli for wearable applications. This work presents a novel strategy for extremely sensitive pressure sensors based on the composite film with local changes in curved 3D carbon nanotube (CNT) structure via expandable microspheres. The sensitivity is significantly enhanced by the synergetic effects of heterogeneous contact of the microdome structure and changes of percolation network within the curved 3D CNT structure. The finite-element method simulation is used to comprehend the relationships between the sensitivity and mechanical/electrical behavior of microdome structure under the applied pressure. The sensor shows an excellent sensitivity (571.64 kPa-1 ) with fast response time (85 ms), great repeatability, and long-term stability. Using the developed sensor, a wireless wearable health monitoring system to avoid carpel tunnel syndrome is built, and a multi-array pressure sensor for realizing a variety of movements in real-time is demonstrated.

Correction: A wearable colorimetric sweat pH sensor-based smart textile for health state diagnosis.

Correction for 'A wearable colorimetric sweat pH sensor-based smart textile for health state diagnosis' by Ji-Hwan Ha et al., Mater. Horiz., 2023, 10, 4163-4171, https://doi.org/10.1039/d3mh00340j.

Recent Advances in Sensor-Actuator Hybrid Soft Systems: Core Advantages, Intelligent Applications, and Future Perspectives.

The growing demand for soft intelligent systems, which have the potential to be used in a variety of fields such as wearable technology and human-robot interaction systems, has spurred the development of advanced soft transducers. Among soft systems, sensor-actuator hybrid systems are considered the most promising due to their effective and efficient performance, resulting from the synergistic and complementary interaction between their sensor and actuator components. Recent research on integrated sensor and actuator systems has resulted in a range of conceptual and practical soft systems. This review article provides a comprehensive analysis of recent advances in sensor and actuator integrated systems, which are grouped into three categories based on their primary functions: i) actuator-assisted sensors for intelligent detection, ii) sensor-assisted actuators for intelligent movement, and iii) sensor-actuator interactive devices for a hybrid of intelligent detection and movement. In addition, several bottlenecks in current studies are discussed, and prospective outlooks, including potential applications, are presented. This categorization and analysis will pave the way for the advancement and commercialization of sensor and actuator-integrated systems.

A wearable colorimetric sweat pH sensor-based smart textile for health state diagnosis.

Sweat pH is an important indicator for diagnosing disease states, such as cystic fibrosis. However, conventional pH sensors are composed of large brittle mechanical parts and need additional instruments to read signals. These pH sensors have limitations for practical wearable applications. In this study, we propose wearable colorimetric sweat pH sensors based on curcumin and thermoplastic-polyurethane (C-TPU) electrospun-fibers to diagnose disease states by sweat pH monitoring. This sensor aids in pH monitoring by changing color in response to chemical structure variation from enol to di-keto form via H-atom separation. Its chemical structure variation changes the visible color due to light absorbance and reflectance changes. Furthermore, it can rapidly and sensitively detect sweat pH due to its superior permeability and wettability. By O2 plasma activation and thermal pressing, this colorimetric pH sensor can be easily attached to various fabric substrates such as swaddling and patient clothing via surface modification and mechanical interlocking of C-TPU. Furthermore, the diagnosable clothing is durable and reusable enough to neutral washing conditions due to the reversible pH colorimetric sensing performance by restoring the enol form of curcumin. This study contributes to the development of smart diagnostic clothing for cystic fibrosis patients who require continuous sweat pH monitoring.

Nanotransfer-on-Things: From Rigid to Stretchable Nanophotonic Devices.

The growing demand for nanophotonic devices has driven the advancement of nanotransfer printing (nTP) technology. Currently, the scope of nTP is limited to certain materials and substrates owing to the temperature, pressure, and chemical bonding requirements. In this study, we developed a universal nTP technique utilizing covalent bonding-based adhesives to improve the adhesion between the target material and substrate. Additionally, the technique employed plasma-based selective etching to weaken the adhesion between the mold and target material, thereby enabling the reliable modulation of the relative adhesion forces, regardless of the material or substrate. The technique was evaluated by printing four optical materials on nine substrates, including rigid, flexible, and stretchable substrates. Finally, its applicability was demonstrated by fabricating a ring hologram, a flexible plasmonic color filter, and extraordinary optical transmission-based strain sensors. The high accuracy and reliability of the proposed nTP method were verified by the performance of nanophotonic devices that closely matched numerical simulation results.

Nanoscale three-dimensional fabrication based on mechanically guided assembly.

The growing demand for complex three-dimensional (3D) micro-/nanostructures has inspired the development of the corresponding manufacturing techniques. Among these techniques, 3D fabrication based on mechanically guided assembly offers the advantages of broad material compatibility, high designability, and structural reversibility under strain but is not applicable for nanoscale device printing because of the bottleneck at nanofabrication and design technique. Herein, a configuration-designable nanoscale 3D fabrication is suggested through a robust nanotransfer methodology and design of substrate's mechanical characteristics. Covalent bonding-based two-dimensional nanotransfer allowing for nanostructure printing on elastomer substrates is used to address fabrication problems, while the feasibility of configuration design through the modulation of substrate's mechanical characteristics is examined using analytical calculations and numerical simulations, allowing printing of various 3D nanostructures. The printed nanostructures exhibit strain-independent electrical properties and are therefore used to fabricate stretchable H2 and NO2 sensors with high performances stable under external strains of 30%.

Battery-Free, Wireless, Ionic Liquid Sensor Arrays to Monitor Pressure and Temperature of Patients in Bed and Wheelchair.

Repositioning is a common guideline for the prevention of pressure injuries of bedridden or wheelchair patients. However, frequent repositioning could deteriorate the quality of patient's life and induce secondary injuries. This paper introduces a method for continuous multi-site monitoring of pressure and temperature distribution from strategically deployed sensor arrays at skin interfaces via battery-free, wireless ionic liquid pressure sensors. The wirelessly delivered power enables stable operation of the ionic liquid pressure sensor, which shows enhanced sensitivity, negligible hysteresis, high linearity and cyclic stability over relevant pressure range. The experimental investigations of the wireless devices, verified by numerical simulation of the key responses, support capabilities for real-time, continuous, long-term monitoring of the pressure and temperature distribution from multiple sensor arrays. Clinical trials on two hemiplegic patients confined on bed or wheelchair integrated with the system demonstrate the feasibility of sensor arrays for a decrease in pressure and temperature distribution under minimal repositioning.

NCMD: Node2vec-Based Neural Collaborative Filtering for Predicting MiRNA-Disease Association.

Numerous studies have reported that micro RNAs (miRNAs) play pivotal roles in disease pathogenesis based on the deregulation of the expressions of target messenger RNAs. Therefore, the identification of disease-related miRNAs is of great significance in understanding human complex diseases, which can also provide insight into the design of novel prognostic markers and disease therapies. Considering the time and cost involved in wet experiments, most recent works have focused on the effective and feasible modeling of computational frameworks to uncover miRNA-disease associations. In this study, we propose a novel framework called node2vec-based neural collaborative filtering for predicting miRNA-disease association (NCMD) based on deep neural networks. Initially, NCMD exploits Node2vec to learn low-dimensional vector representations of miRNAs and diseases. Next, it utilizes a deep learning framework that combines the linear ability of generalized matrix factorization and nonlinear ability of a multilayer perceptron. Experimental results clearly demonstrate the comparable performance of NCMD relative to the state-of-the-art methods according to statistical measures. In addition, case studies on breast cancer, lung cancer and pancreatic cancer validate the effectiveness of NCMD. Extensive experiments demonstrate the benefits of modeling a neural collaborative-filtering-based approach for discovering novel miRNA-disease associations.

MDMF: Predicting miRNA-Disease Association Based on Matrix Factorization with Disease Similarity Constraint.

MicroRNAs (miRNAs) have drawn enormous attention owing to their significant roles in various biological processes, as well as in the pathogenesis of human diseases. Therefore, predicting miRNA-disease associations is a pivotal task for the early diagnosis and better understanding of disease pathogenesis. To date, numerous computational frameworks have been proposed to identify potential miRNA-disease associations without escalating the costs and time required for clinical experiments. In this regard, I propose a novel computational framework (MDMF) for identifying potential miRNA-disease associations using matrix factorization with a disease similarity constraint. To evaluate the performance of MDMF, I calculated the area under the ROC curve (AUCs) in the framework of global and local leave-one-out cross-validation (LOOCV). In conclusion, MDMF achieved reliable AUC values of 0.9147 and 0.8905 for global and local LOOCV, respectively, which was a significant improvement upon the previous methods. Additionally, case studies were conducted on two major human cancers (breast cancer and lung cancer) to validate the effectiveness of MDMF. Comprehensive experimental results demonstrate that MDMF not only discovers miRNA-disease associations efficiently but also deciphers the underlying roles of miRNAs in the pathogenesis of diseases at a system level.

Spherical Micro/Nano Hierarchical Structures for Energy and Water Harvesting Devices.

Three-dimensional (3D) hierarchical structures have been explored for various applications owing to the synergistic effects of micro- and nanostructures. However, the development of spherical micro/nano hierarchical structures (S-HSs), which can be used as energy/water harvesting systems and sensing devices, remains challenging owing to the trade-off between structural complexity and fabrication difficulty. This paper presents a new strategy for facile, scalable S-HS fabrication using a thermal expansion of microspheres and nanopatterned structures. When a specific temperature is applied to a composite film of microspheres and elastomers with nanopatterned surfaces, microspheres are expanded and 3D spherical microstructures are generated. Various nanopatterns and densities of spherical microstructures can thereby be quantitatively controlled. The fabricated S-HSs have been used in renewable electrical energy harvesting and sustainable water management applications. Compared to a triboelectric nanogenerator (TENG) with bare film, the S-HS-based TENG exhibited 4.48 times higher triboelectric performance with high mechanical durability. Furthermore, an S-HS is used as a water harvesting device to capture water in a fog environment. The water collection rate is dramatically enhanced by the increased surface area and locally concentrated vapor diffusion flux due to the spherical microstructures.

Biocompatible Nanotransfer Printing Based on Water Bridge Formation in Hyaluronic Acid and Its Application to Smart Contact Lenses.

Many conventional micropatterning and nanopatterning techniques employ toxic chemicals, rendering them nonbiocompatible and unsuited for biodevice production. Herein the formation of water bridges on the surface of hyaluronic acid (HA) films is exploited to develop a transfer-based nanopatterning method applicable to diverse structures and materials. The HA film surface, made deformable via water bridge generation, is brought into contact with a functional material and subjected to thermal treatment, which results in film shrinkage, allowing a robust pattern transfer. The proposed biocompatible method, which avoids the use of extra chemicals, enables the transfer of nanoscale, microscale, and thin-film structures as well as functional materials such as metals and metal oxides. A nanopatterned HA film is transferred onto a moisture-containing contact lens to fabricate smart contact lenses with unique optical characteristics of rationally designed optical nanopatterns. These lenses demonstrated binocular parallax-induced stereoscopy via nanoline array polarization and acted as cutoff filters, with nanodot arrays, capable of treating Irlen syndrome.

Flexible Carbon Nanotube/Polydimethylsiloxane Composite for the De-Icing of Airplane Wings.

In the aviation industry, the process of de-icing is critical for stable flying because of the occurrence of airplane icing. To solve the icing problem, an electrical heating system is applied for airplane de-icing. Among the materials used in the electrical heating system, carbon-nanotube polymer composites are appropriate for an ice-prevention system owing to their rapid heating properties and flexibility. In this study, we fabricated a flexible carbon-nanotube/polydimethylsiloxane composite with a high content of carbon nanotube (20 wt%) for airplane de-icing. The high-load carbon nanotube composite was fabricated using a three-roll milling method, resulting in uniform dispersion of carbon nanotubes in the polymer matrix. The carbon nanotube/polydimethylsiloxane composites exhibited uniform and stable heating performance (from room temperature to 100 °C for 25 s without thermal aggregation). In addition, the carbon nanotube/polydimethylsiloxane composite is suitable for application to the curved surface of airfoils. For the de-icing experiments, a small airplane wing consisting of carbon nanotube/polydimethylsiloxane composite as a heating unit was fabricated with a scale ratio of 15:1. We conducted electrical heating and de-icing experiments using the developed airplane-wing system for actual anti-icing/de-icing applications.

Flexible Thin Carbon Nanotube Web Film for Curved Heating Elements Under High Temperature Conditions.

Heating elements need a rapid heating property and long-term cycle stability when subjected to extreme temperatures. Carbon nanotube-based films can be used as ideal heating units owing to their superior electrical and thermal properties. However, carbon nanotube polymer composites are not appropriate for extreme conditions such as high temperatures (300 °C) due to the poor thermal stability of the polymer matrix. In this study, we fabricated a carbon nanotube web film, comprising heating elements consisting of pure carbon nanotubes, through the direct spinning method. The carbon nanotube web film has a microscale thickness. The carbon nanotube web film showed flexibility at high temperatures, while a fracture occurred in the case of the carbon nanotube polymer composite. We conducted electrical heating experiments on the curved carbon nanotube web film to observe the heating uniformity and flexibility. The heating test is conducted on various curved form heaters. The carbon nanotube web film showed rapid heating properties and a uniform heat distribution (temperature departure of less than 3%) without thermal aggregation. The curved heating units can be utilized in various applications such as functional clothes and de-icing systems having curved surfaces.

Strain­Sensing Properties of Multi­Walled Carbon Nanotube/Polydimethylsiloxane Composites with Different Aspect Ratio and Filler Contents.

For filler composite systems used in strain sensor applications, piezoresistive effect, strain hysteresis, and repeatability are critical factors, which have to be clearly evaluated and understood. To investigate the effects of the aspect ratio and content of a multi­walled carbon nanotube (MWCNT) on the strain sensor properties of the composite, MWCNT/Polydimethylsiloxane (PDMS) composites with varying filler contents and aspect ratios were fabricated. In order to uniformly disperse MWCNTs on the polymer matrix, we used a three­roll milling method to generate high shear force for de­bundling MWCNTs. Mechanical and electrical properties of the MWCNT composites were evaluated for each case. In addition, through the cyclic stretching test, we optimized the strain­sensing properties of the MWCNT composites by considering their piezoresistive effects and strain hysteresis.

Improved Prediction of miRNA-Disease Associations Based on Matrix Completion with Network Regularization.

The identification of potential microRNA (miRNA)-disease associations enables the elucidation of the pathogenesis of complex human diseases owing to the crucial role of miRNAs in various biologic processes and it yields insights into novel prognostic markers. In the consideration of the time and costs involved in wet experiments, computational models for finding novel miRNA-disease associations would be a great alternative. However, computational models, to date, are biased towards known miRNA-disease associations; this is not suitable for rare miRNAs (i.e., miRNAs with a few known disease associations) and uncommon diseases (i.e., diseases with a few known miRNA associations). This leads to poor prediction accuracies. The most straightforward way of improving the performance is by increasing the number of known miRNA-disease associations. However, due to lack of information, increasing attention has been paid to developing computational models that can handle insufficient data via a technical approach. In this paper, we present a general framework-improved prediction of miRNA-disease associations (IMDN)-based on matrix completion with network regularization to discover potential disease-related miRNAs. The success of adopting matrix factorization is demonstrated by its excellent performance in recommender systems. This approach considers a miRNA network as additional implicit feedback and makes predictions for disease associations relevant to a given miRNA based on its direct neighbors. Our experimental results demonstrate that IMDN achieved excellent performance with reliable area under the receiver operating characteristic (ROC) area under the curve (AUC) values of 0.9162 and 0.8965 in the frameworks of global and local leave-one-out cross-validations (LOOCV), respectively. Further, case studies demonstrated that our method can not only validate true miRNA-disease associations but also suggest novel disease-related miRNA candidates.

Combinatorial feature embedding based on CNN and LSTM for biomedical named entity recognition.

With the rapid advancement of technology and the necessity of processing large amounts of data, biomedical Named Entity Recognition (NER) has become an essential technique for information extraction in the biomedical field. NER, which is a sequence-labeling task, has been performed using various traditional techniques including dictionary-, rule-, machine learning-, and deep learning-based methods. However, as existing biomedical NER models are insufficient to handle new and unseen entity types from the growing biomedical data, the development of more effective and accurate biomedical NER models is being widely researched. Among biomedical NER models utilizing deep learning approaches, there have been only a few studies involving the design of high-level features in the embedding layer. In this regard, herein, we propose a deep learning NER model that effectively represents biomedical word tokens through the design of a combinatorial feature embedding. The proposed model is based on Bidirectional Long Short-Term Memory (bi-LSTM) with Conditional Random Field (CRF) and enhanced by integrating two different character-level representations extracted from a Convolutional Neural Network (CNN) and bi-LSTM. Additionally, an attention mechanism is applied to the model to focus on the relevant tokens in the sentence, which alleviates the long-term dependency problem of the LSTM model and allows effective recognition of entities. The proposed model was evaluated on two benchmark datasets, the JNLPBA and NCBI-Disease, and a comparative analysis with the existing models is performed. The proposed model achieved a relatively higher performance with an F1-score of 86.93% in case of NCBI-Disease, and a competitive performance for the JNLPBA with an F1-score of 75.31%.

IMIPMF: Inferring miRNA-disease interactions using probabilistic matrix factorization.

Recently, increasing evidence have reported that microRNAs (miRNAs) play key roles in a variety of biological processes. Therefore, the identification of novel miRNA-disease associations can shed new light on disease etiology and pathogenesis. Till now, various computational methods have been proposed to predict potential miRNA-disease associations by reducing the experimental costs and time consumption. However, most existing methods are highly dependent on known miRNA-disease associations. Therefore, the prediction of new miRNAs (i.e., miRNAs without known associated diseases) and new diseases (i.e., diseases without known associated miRNAs) has become challenging. In this paper, we present IMIPMF, a novel method for predicting miRNA-disease associations using probabilistic matrix factorization (PMF), which is a machine learning technique that is widely used in recommender systems. Predicting the rating scores that a user may assign to each item in a recommender system is analogous to predicting miRNA-disease associations. By applying PMF, our model not only identifies novel miRNA-disease associations, but also overcomes the common problem of incompatibility with miRNAs without any known associated disease, which was a limitation of most previous computational methods. We demonstrated that our proposed model achieved a high performance with a reliable AUC value of 0.891 by performing 5-fold cross-validation. Overall, IMIPMF is a high-performance machine-learning-based model for predicting miRNA-disease associations, although it only considers known miRNA-disease associations and miRNA expression data.

Development of Multi-Functional Graphene Polymer Composites Having Electromagnetic Interference Shielding and De-Icing Properties.

We developed a multi-functional graphene composite with electromagnetic interference (EMI) shielding and de-icing properties. Two-dimensional graphene fillers were homogeneously dispersed in a polymer by three-roll milling. The electrical properties and percolation threshold of the graphene composites were measured with various graphene contents. The variation in the EMI shielding properties of the graphene composites with respect to the filler content was measured. The shielding efficiency improved with increasing graphene filler content. Furthermore, we conducted electrical heating tests on the graphene composites. The composites could be heated rapidly to 200 °C by electrical Joule heating with low electric power because of the high electrical conductivity of the composite. Moreover, the composite film was suitable for application in a de-icing unit because of its rapid and homogenous heating performance.