Scott-Russel Linkage-Based Triboelectric Self-Powered Sensor for Contact Material-Independent Force Sensing and Tactile Recognition.

The rapid growth of Internet of Things (IoT) in recent years has increased demand for various sensors to collect a wide range of data. Among various sensors, the demand for force sensors that can recognize physical phenomena in 3D space has notably increased. Recent research has focused on developing energy harvesting methods for sensors to address their maintenance problems. Triboelectric nanogenerator (TENG) based force sensors are a promising solution for converting external motion into electrical signals. However, conventional TENG-based force sensors that use the signal peak can negatively affect data accuracy. In this study, a Scott-Russell linkage-inspired TENG (SRI-TENG) is developed. The SRI-TENG has completely separate signal generation and measurement sections, and the number of peaks in the electrical output is measured to prevent disturbing output signals. In addition, the lubricant liquid enhances durability, enabling stable force signal measurements for 270 000 cycles. The SRI system demonstrates consistent peak counts and high accuracy across different contacting surfaces, indicating that it can function as a contact material-independent self-powered force sensor. Furthermore, using a deep learning method, it is demonstrated that it can function as a multimodal sensor by realizing the tactile properties of various materials.

Imbalanced spectral data analysis using data augmentation based on the generative adversarial network.

Spectroscopic techniques generate one-dimensional spectra with distinct peaks and specific widths in the frequency domain. These features act as unique identities for material characteristics. Deep neural networks (DNNs) has recently been considered a powerful tool for automatically categorizing experimental spectra data by supervised classification to evaluate material characteristics. However, most existing work assumes balanced spectral data among various classes in the training data, contrary to actual experiments, where the spectral data is usually imbalanced. The imbalanced training data deteriorates the supervised classification performance, hindering understanding of the phase behavior, specifically, sol-gel transition (gelation) of soft materials and glycomaterials. To address this issue, this paper applies a novel data augmentation method based on a generative adversarial network (GAN) proposed by the authors in their prior work. To demonstrate the effectiveness of the proposed method, the actual imbalanced spectral data from Pluronic F-127 hydrogel and Alpha-Cyclodextrin hydrogel are used to classify the phases of data. Specifically, our approach improves 8.8%, 6.4%, and 6.2% of the performance of the existing data augmentation methods regarding the classifier's F-score, Precision, and Recall on average, respectively. Specifically, our method consists of three DNNs: the generator, discriminator, and classifier. The method generates samples that are not only authentic but emphasize the differentiation between material characteristics to provide balanced training data, improving the classification results. Based on these validated results, we expect the method's broader applications in addressing imbalanced measurement data across diverse domains in materials science and chemical engineering.

Predicting the stereoselectivity of chemical reactions by composite machine learning method.

Stereoselective reactions have played a vital role in the emergence of life, evolution, human biology, and medicine. However, for a long time, most industrial and academic efforts followed a trial-and-error approach for asymmetric synthesis in stereoselective reactions. In addition, most previous studies have been qualitatively focused on the influence of steric and electronic effects on stereoselective reactions. Therefore, quantitatively understanding the stereoselectivity of a given chemical reaction is extremely difficult. As proof of principle, this paper develops a novel composite machine learning method for quantitatively predicting the enantioselectivity representing the degree to which one enantiomer is preferentially produced from the reactions. Specifically, machine learning methods that are widely used in data analytics, including Random Forest, Support Vector Regression, and LASSO, are utilized. In addition, the Bayesian optimization and permutation importance tests are provided for an in-depth understanding of reactions and accurate prediction. Finally, the proposed composite method approximates the key features of the available reactions by using Gaussian mixture models, which provide suitable machine learning methods for new reactions. The case studies using the real stereoselective reactions show that the proposed method is effective and provides a solid foundation for further application to other chemical reactions.

Robustly Adherable Hierarchical Nanostructures via Self-Bonding and Self-Texturing of Aluminum Nitride for Applications in Highly Efficient Oil/Water Separation.

The release of wastewater containing oily contaminants into water bodies and soils severely threatens the environment and human health. Although several conventional techniques are used in treating oil/water mixtures and emulsions, these methods are often expensive, time-consuming, and inefficient. Porous membranes or sponges are widely used in filtration or absorption, but their use is limited by their low separation efficiencies and secondary contamination. Recently, a novel technology that is designed to selectively separate oil from oil/water mixtures or emulsions by using materials with special wetting surfaces was developed. Superwetting surfaces may be used to selectively separate oils from emulsions. This approach enables the use of materials with relatively large pores, resulting in high throughput properties and efficiencies. In this study, a facile method is proposed for use in preparing a superhydrophobic-superoleophilic felt fabric for utilization in separating oil/water mixtures and emulsions. By hydrolyzing aluminum nitride nanopowders, the desired micro-/nanostructures may be successfully fabricated and firmly attached to a fabric surface without using a binder resin. This results in various materials with special wetting properties, regardless of their sizes and shapes and the successful separation of oil and water from oil/water mixtures and emulsions in harsh environments. This approach exhibits promise as a low-cost, scalable, and efficient method of separating oily wastewater, with the potential for use in wider industrial applications.

Self-Scalable Tanh (Stan): Multi-Scale Solutions for Physics-Informed Neural Networks.

Differential equations are fundamental in modeling numerous physical systems, including thermal, manufacturing, and meteorological systems. Traditionally, numerical methods often approximate the solutions of complex systems modeled by differential equations. With the advent of modern deep learning, Physics-informed Neural Networks (PINNs) are evolving as a new paradigm for solving differential equations with a pseudo-closed form solution. Unlike numerical methods, the PINNs can solve the differential equations mesh-free, integrate the experimental data, and resolve challenging inverse problems. However, one of the limitations of PINNs is the poor training caused by using the activation functions designed typically for purely data-driven problems. This work proposes a scalable tanh-based activation function for PINNs to improve learning the solutions of differential equations. The proposed Self-scalable tanh (Stan) function is smooth, non-saturating, and has a trainable parameter. It can allow an easy flow of gradients and enable systematic scaling of the input-output mapping during training. Various forward problems to solve differential equations and inverse problems to find the parameters of differential equations demonstrate that the Stan activation function can achieve better training and more accurate predictions than the existing activation functions for PINN in the literature.

Recent Advances in Triboelectric Nanogenerators: From Technological Progress to Commercial Applications.

Serious climate changes and energy-related environmental problems are currently critical issues in the world. In order to reduce carbon emissions and save our environment, renewable energy harvesting technologies will serve as a key solution in the near future. Among them, triboelectric nanogenerators (TENGs), which is one of the most promising mechanical energy harvesters by means of contact electrification phenomenon, are explosively developing due to abundant wasting mechanical energy sources and a number of superior advantages in a wide availability and selection of materials, relatively simple device configurations, and low-cost processing. Significant experimental and theoretical efforts have been achieved toward understanding fundamental behaviors and a wide range of demonstrations since its report in 2012. As a result, considerable technological advancement has been exhibited and it advances the timeline of achievement in the proposed roadmap. Now, the technology has reached the stage of prototype development with verification of performance beyond the lab scale environment toward its commercialization. In this review, distinguished authors in the world worked together to summarize the state of the art in theory, materials, devices, systems, circuits, and applications in TENG fields. The great research achievements of researchers in this field around the world over the past decade are expected to play a major role in coming to fruition of unexpectedly accelerated technological advances over the next decade.

Ultrahigh Performance, Serially Stackable, Breeze Driven Triboelectric Generator via Ambient Air Ionizing Channel.

Currently, wind energy harvesting is in the limelight. However, with the existing electromagnetic wind generators, it is difficult to harvest multifariously-wasted breezes. To harvest energy from winds at a wide range of speeds, wind-driven triboelectric nanogenerators (TENGs) are studied. However, a critical limitation of general wind-driven TENGs is that their power output is low. Therefore, an innovative strategy is necessary to generate high output power even from breeze. Herein, an approach to test a charge-polarization-based flutter-driven TENG (CPF-TENG) with ambient air ionizing channel (AAIC) is reported. Owing to AAIC, the device generates peak voltage and current outputs of 2000 V and 4 A, respectively. Moreover, because the proposed CPF-TENG can generate power from breeze, it can be stacked in series to completely harvest wind energy. The stacked CPF-TENG is demonstrated to operate 3000 light-emitting diodes (LEDs) and 12 hygrometers, separately, and produce hydrogen at a rate of 342.3 µL h-1 with the electrolysis cell.

Lightweight mobile stick-type water-based triboelectric nanogenerator with amplified current for portable safety devices.

Due to its abundance, mechanical energy is a promising ambient energy source. Triboelectric nanogenerators (TENGs) represent an effective mechanical energy harvesting method based on the use of contact electrification. The existing liquid-based TENGs can operate robustly without surface damage; however, the output of these TENGs is considerably smaller than that of solid-based TENGs. Notably, liquid-based TENGs in which the liquid directly contacts the conductive material can produce an electrical current of more than few mA. However, the liquid reservoir must have an adequate volume, and sufficient space must be provided for the liquid to move for generating the electrical output. To ensure a compact and lightweight design and produce electrical output in the low input frequency range, we introduce a mobile stick-type water-based TENG (MSW-TENG). The proposed MSW-TENG can generate an open-circuit voltage and closed-circuit current of up to 710 V and 2.9 mA, respectively, and be utilized as self-powered safety device. The findings of this study can promote the implementation of TENGs in everyday applications.

A portable device for water-sloshing-based electricity generation based on charge separation and accumulation.

Hydropower generation is a well-known electricity generation technique that uses Faraday's law and hydraulic turbines. Recently, a triboelectrification-based electricity generation device, using water as the triboelectric material (W-TEG) was developed. In addition to the enhancement of the electrical output performance through the operation mechanism, the characteristics of the W-TEG must be examined at the design level to facilitate its portable application. Therefore, in this work, we developed a portable water-sloshing-based electricity generator (PS-EG) that can produce a high electric output and achieved its closed-loop circuit design and quantitative analysis for portable applications. The proposed PS-EG produced peak open-circuit voltage (V OC ) and closed-circuit current (I CC ) of up to 484 V and 4.1 mA, respectively, when subjected to vibrations of 2 Hz. The proposed PS-EG can be effectively used as an auxiliary power source for small electronics and sensors.

Triangulated Cylinder Origami-Based Piezoelectric/Triboelectric Hybrid Generator to Harvest Coupled Axial and Rotational Motion.

Piezoelectric nanogenerators (PENGs) and triboelectric nanogenerators (TENGs) are representative technologies that can harvest mechanical energy. In general, piezoelectric/triboelectric hybrid generators can harvest considerable energy with a limited input; however, PENGs and TENGs entail different requirements for harvesting energy. Specifically, PENGs produce a large output when a large mechanical strain is applied, and TENGs require a large surface area to produce a high power. Therefore, it is necessary to develop an innovative strategy in terms of the structural design to satisfy the requirements of both PENGs and TENGs. In this study, we developed a triangulated cylinder origami-based piezoelectric/triboelectric hybrid generator (TCO-HG) with an origami structure to enable effective energy harvesting. The proposed structure consists of a vertical contact-separation TENG on the surface of the triangulated cylinder, PENG on the inner hinge, and rotational TENG on the top substrate to harvest mechanical energy from each motion. Each generator could produce a separate electrical output with a single input. The TCO-HG could charge a 22 µF commercial capacitor and power 60 LEDs when operated.

Versatile surface for solid-solid/liquid-solid triboelectric nanogenerator based on fluorocarbon liquid infused surfaces.

The triboelectric nanogenerator (TENG) is a recent mechanical energy harvesting technology that has been attracting significant attention. Its working principle involves the combination of triboelectrification and electrostatic induction. The TENG can harvest electrical energy from both solid-solid and liquid-solid contact TENGs. Due to their physical difference, triboelectric materials in the solid-solid TENG need to have high mechanical properties and the surface of the liquid-solid contact TENG should repel water. Therefore, the surface of the TENG must be versatile for applications in both solid-solid and liquid-solid contact environments. In this work, we develop a solid-solid/liquid-solid convertible TENG that has a slippery liquid-infused porous surface (SLIPS) at the top of the electrode. The SLIPS consists of a HDFS coated hierarchical Al(OH)3 structure and fluorocarbon liquid. The convertible TENG developed in this study is capable of harvesting electricity from both solid-solid and liquid-solid contacts due to the high mechanical property of Al(OH)3 and the water-based liquid repelling nature of the SLIPS. When the contact occurs in freestanding mode, electrical output was generated through solid-solid/liquid-solid sliding motions. The convertible TENG can harvest electricity from both solid-solid and liquid-solid contacts; thus, it can be a unified solution for TENG surface fabrication.

Hand-Driven Gyroscopic Hybrid Nanogenerator for Recharging Portable Devices.

With the rise of portable and wearable electronics, a fast-charging, long-lasting power solution is needed; thus, there are attempts to harvest energy from the ambient environment. Mechanical energy harvesting through piezoelectric and triboelectric nanogenerators (PENG and TENG) is a promising approach due to their light weight, low cost, and high-power density in comparison to other technologies. Both types of generators are capable of charging portable and smart devices on their own by converting mechanical energy into electricity. However, most previous methods have excessive input conditions, such as high rpm and input frequency, that can be only applied with other actuators. Here, a hand-held gyroscopic generator is presented that uses the gyroscopic principle to reach a rotation rate above 8000 rpm with only hand input. The generator comprises a rotating flywheel inside a casing. Both the flywheel and casing have a TENG, and with a hybrid generator, electrical power is produced from rotation, vibration, and centrifugal force during operation. The device shows a consistent open-circuit voltage (V OC) of 90 V and a closed-circuit current (I CC) of 11 µA with a frequency of 200 Hz. As a stand-alone device, this generator can power portable sensors and smartphones through hand rotation.

Superhydrophobic Water-Solid Contact Triboelectric Generator by Simple Spray-On Fabrication Method.

Energy harvesting is a method of converting energy from ambient environment into useful electrical energy. Due to the increasing number of sensors and personal electronics, energy harvesting technologies from various sources are gaining attention. Among energy-harvesting technologies, triboelectric nanogenerator (TENG) was introduced as a device that can effectively generate electricity from mechanical motions by contact-electrification. Particularly, liquid-solid contact TENGs, which use the liquid itself as a triboelectric material, can overcome the inevitable friction wear between two solid materials. Using a commercial aerosol hydrophobic spray, liquid-solid contact TENGs, with a superhydrophobic surface (contact angle over 160°) can be easily fabricated with only a few coating processes. To optimize the fabrication process, the open-circuit voltage of sprayed superhydrophobic surfaces was measured depending on the number of coating processes. To demonstrate the simple fabrication and applicability of this technique on random 3D surfaces, a liquid-solid contact TENG was fabricated on the brim of a cap (its complicated surface structure is due to the knitted strings). This simple sprayed-on superhydrophobic surface can be a possible solution for liquid-solid contact TENGs to be mass produced and commercialized in the future.

Highly reliable wind-rolling triboelectric nanogenerator operating in a wide wind speed range.

Triboelectric nanogenerators are aspiring energy harvesting methods that generate electricity from the triboelectric effect and electrostatic induction. This study demonstrates the harvesting of wind energy by a wind-rolling triboelectric nanogenerator (WR-TENG). The WR-TENG generates electricity from wind as a lightweight dielectric sphere rotates along the vortex whistle substrate. Increasing the kinetic energy of a dielectric converted from the wind energy is a key factor in fabricating an efficient WR-TENG. Computation fluid dynamics (CFD) analysis is introduced to estimate the precise movements of wind flow and to create a vortex flow by adjusting the parameters of the vortex whistle shape to optimize the design parameters to increase the kinetic energy conversion rate. WR-TENG can be utilized as both a self-powered wind velocity sensor and a wind energy harvester. A single unit of WR-TENG produces open-circuit voltage of 11.2 V and closed-circuit current of 1.86 µA. Additionally, findings reveal that the electrical power is enhanced through multiple electrode patterns in a single device and by increasing the number of dielectric spheres inside WR-TENG. The wind-rolling TENG is a novel approach for a sustainable wind-driven TENG that is sensitive and reliable to wind flows to harvest wasted wind energy in the near future.

Cylindrical Water Triboelectric Nanogenerator via Controlling Geometrical Shape of Anodized Aluminum for Enhanced Electrostatic Induction.

We demonstrate a cylindrical water triboelectric nanogenerator (CW-TENG) that generates sustainable electrical output. The inner surface of the cylinder was patterned into superhydrophobic and hydrophilic parts to control water flow inside the packaged design of CW-TENG. Here, various thicknesses and roughnesses of the superhydrophobic surface, generated using aluminum oxide nanostructures for enhanced electrostatic induction, were measured to obtain the maximum output and superhydrophobicity. Also, we demonstrate the possibility of using a hydrophilic surface for energy harvesting and as a water reservoir in the packaged design.