Nanoscale Modulation of Friction and Triboelectrification via Surface Nanotexturing.

Nanoscale contact electrification (CE) of elastomer surfaces and the resulting tribocharge formation are important in many branches of nanotechnology but their mechanism is not fully clarified. In this Letter, we investigate the mechanism using the recently discovered phenomenon of replica molding-induced nanoscale CE. By generating tribocharge distributions patterned in close correlation with the interfacial nanotextures, the phenomenon provides well-defined targets for the investigation. By applying a variety of scanning probe microscopy techniques (AFM/KPFM/EFM) and finite element modeling (FEM) to the tribocharge distributions, we extract a process model that can explain how their patterns are formed and affected by the interfacial nanotexture's morphology. It turns out that the cumulative distance of the elastomer's tangential sliding during the interfacial separation plays the key role in shaping the tribocharge's distribution pattern. The model proves remarkably universal, staying valid to nanotextures all the way down in the sub-10 nm regime. This replica molding-induced CE also turns out to be an effective tool for sculpting nanoscale tribocharge distributions into unconventional forms, such as rings, partial eclipses, and dumbbells. Both the model and the technique will prove useful in many areas of nanotechnology.

Analytical Investigation of Replica-Molding-Enabled Nanopatterned Tribocharging Process on Soft-Material Surfaces.

Nanopatterned tribocharge can be generated on the surface of elastomers through their replica molding with nanotextured molds. Despite its vast application potential, the physical conditions enabling the phenomenon have not been clarified in the framework of analytical mechanics. Here, we explain the final tribocharge pattern by separately applying two models, namely cohesive zone failure and cumulative fracture energy, as a function of the mold nanotexture's aspect ratio. These models deepen our understanding of the triboelectrification phenomenon.

Treadmill exercise represses neuronal cell death and inflammation during Aß-induced ER stress by regulating unfolded protein response in aged presenilin 2 mutant mice.

Alzheimer's disease (AD) is characterized by the deposition of aggregated amyloid-beta (Aß), which triggers a cellular stress response called the unfolded protein response (UPR). The UPR signaling pathway is a cellular defense system for dealing with the accumulation of misfolded proteins but switches to apoptosis when endoplasmic reticulum (ER) stress is prolonged. ER stress is involved in neurodegenerative diseases including AD, but the molecular mechanisms of neuronal apoptosis and inflammation by Aß-induced ER stress to exercise training are not fully understood. Here, we demonstrated that treadmill exercise (TE) prevented PS2 mutation-induced memory impairment and reduced Aß-42 deposition through the inhibition of ß-secretase (BACE-1) and its product, C-99 in cortex and/or hippocampus of aged PS2 mutant mice. We also found that TE down-regulated the expression of GRP78/Bip and PDI proteins and inhibited activation of PERK, eIF2α, ATF6α, sXBP1 and JNK-p38 MAPK as well as activation of CHOP, caspase-12 and caspase-3. Moreover, TE up-regulated the expression of Bcl-2 and down-regulated the expressions of Bax in the hippocampus of aged PS2 mutant mice. Finally, the generation of TNFα and IL-1α and the number of TUNEL-positive cells in the hippocampus of aged PS2 mutant mice was also prevented or decreased by TE. These results showed that TE suppressed the activation of UPR signaling pathways as well as inhibited the apoptotic pathways of the UPR and inflammatory response following Aß-induced ER stress. Thus, therapeutic strategies that modulate Aß-induced ER stress through TE could represent a promising approach for the prevention or treatment of AD.

Sharpen data-driven prediction rules of individual large earthquakes with aid of Fourier and Gauss.

Predicting individual large earthquakes (EQs)' locations, magnitudes, and timing remains unreachable. The author's prior study shows that individual large EQs have unique signatures obtained from multi-layered data transformations. Via spatio-temporal convolutions, decades-long EQ catalog data are transformed into pseudo-physics quantities (e.g., energy, power, vorticity, and Laplacian), which turn into surface-like information via Gauss curvatures. Using these new features, a rule-learning machine learning approach unravels promising prediction rules. This paper suggests further data transformation via Fourier transformation (FT). Results show that FT-based new feature can help sharpen the prediction rules. Feasibility tests of large EQs ([Formula: see text] 6.5) over the past 40 years in the western U.S. show promise, shedding light on data-driven prediction of individual large EQs. The handshake among ML methods, Fourier, and Gauss may help answer the long-standing enigma of seismogenesis.

Tackling Multi-Physics Nano-Scale Phenomena in Capillary Force Lithography with Small Data by Hybrid Intelligence.

The scientific community has been looking for novel approaches to develop nanostructures inspired by nature. However, due to the complicated processes involved, controlling the height of these nanostructures is challenging. Nanoscale capillary force lithography (CFL) is one way to use a photopolymer and alter its properties by exposing it to ultraviolet radiation. Nonetheless, the working mechanism of CFL is not fully understood due to a lack of enough information and first principles. One of these obscure behaviors is the sudden jump phenomenon-the sudden change in the height of the photopolymer depending on the UV exposure time and height of nano-grating (based on experimental data). This paper uses known physical principles alongside artificial intelligence to uncover the unknown physical principles responsible for the sudden jump phenomenon. The results showed promising results in identifying air diffusivity, dynamic viscosity, surface tension, and electric potential as the previously unknown physical principles that collectively explain the sudden jump phenomenon.

Pursuit of hidden rules behind the irregularity of nano capillary lithography by hybrid intelligence.

Nature finds a way to leverage nanotextures to achieve desired functions. Recent advances in nanotechnologies endow fascinating multi-functionalities to nanotextures by modulating the nanopixel's height. But nanoscale height control is a daunting task involving chemical and/or physical processes. As a facile, cost-effective, and potentially scalable remedy, the nanoscale capillary force lithography (CFL) receives notable attention. The key enabler is optical pre-modification of photopolymer's characteristics via ultraviolet (UV) exposure. Still, the underlying physics of the nanoscale CFL is not well understood, and unexplained phenomena such as the "forbidden gap" in the nano capillary rise (unreachable height) abound. Due to the lack of large data, small length scales, and the absence of first principles, direct adoptions of machine learning or analytical approaches have been difficult. This paper proposes a hybrid intelligence approach in which both artificial and human intelligence coherently work together to unravel the hidden rules with small data. Our results show promising performance in identifying transparent, physics-retained rules of air diffusivity, dynamic viscosity, and surface tension, which collectively appear to explain the forbidden gap in the nanoscale CFL. This paper promotes synergistic collaborations of humans and AI for advancing nanotechnology and beyond.

Mechanisms of interactions in pattern-recognition of common glycostructures across pectin-derived heteropolysaccharides by Toll-like receptor 4.

Complex pectin, originating from terrestrial plant cell walls has been attracting research attention as a promising source of a new innate immune modulator. Numerous bioactive polysaccharides associated with pectin are newly reported every year, but the general mechanism of their immunological action remains unclear owing to the complexity and heterogeneity of pectin. Herein, we systematically investigated the interactions in pattern-recognition for common glycostructures of pectic heteropolysaccharides (HPSs) by Toll-like receptors (TLRs). The compositional similarity of glycosyl residues derived from pectic HPS was confirmed by conducting systematic reviews, leading to molecular modeling of representative pectic segments. Via structural investigation, the inner concavity of leucine-rich repeats of TLR4 was predicted to act as a binding motif for carbohydrate recognition, and subsequent simulations predicted the binding modes and conformations. We experimentally demonstrated that pectic HPS exhibits the non-canonical and multivalent binding aspects for TLR4 resulting in receptor activation. Furthermore, we showed that pectic HPSs were selectively clustered with TLR4 during endocytosis, inducing downstream signals to cause phenotypic activation of macrophages. Overall, we have presented a better explanation for the pattern recognition of pectic HPS and further proposed an approach to understand the interaction between complex carbohydrates and proteins.

Agarperoxinols A and B: Two Unprecedented Tricyclic 6/6/7 Rearranged Humulene-Type Sesquiterpenoids That Attenuated the Neuroinflammation in LPS-Stimulated Microglial Models.

Agarperoxinols A and B (1-2), two naturally occurring humulene-type sesquiterpenoids with an unprecedented tricyclic 6/6/7 ring, were discovered from the agarwood of Aquilaria malaccensis. Their structures were unambiguously determined by various spectroscopic data, experimental ECD calculations, and single-crystal X-ray diffraction analysis. Agarperoxinol B showed significant and dose-dependent neuroinflammatory inhibitory effects on various proinflammatory mediators, including NO, TNF-α, IL-6, and IL-1ß, and suppressed iNOS and COX-2 enzymes in LPS-activated microglial cells. A mechanistic study demonstrated that agarperoxinol B remarkably inhibited the phosphorylation of the Akt and JNK signaling pathways. Agarperoxinol B also significantly reduced the expression of the microglial markers Iba-1, COX-2, and TNF-α in the mouse cerebral cortex. Our findings introduce a bioactive compound from natural products that decreases proinflammatory factor production and has application for the treatment of neurodegenerative diseases.

Intestinal Immune Cell Populations, Barrier Function, and Microbiomes in Broilers Fed a Diet Supplemented with Chlorella vulgaris.

This study aimed to evaluate the effects of dietary Chlorella vulgaris (CV) on the distribution of immune cells, intestinal morphology, intestinal barrier function, antioxidant markers, and the cecal microbiome in 10-day-old broiler chickens. A total of 120 day-old Ross 308 male broiler chicks were assigned to two dietary treatments using a randomized complete block design, with body weight as the blocking factor. Birds fed a diet containing CV showed an increase in CD4+ T cells (p < 0.05) compared to those fed the control diet. The relative mRNA expression of intestinal epithelial barrier function-related markers (occludin and avian ß-defensin 5) was elevated (p < 0.05) in the CV-supplemented group compared to the control group. The alpha diversity indices (Chao1 and observed features) of the cecal microbiome in 10-day-old birds increased (p < 0.05), indicating higher richness within the cecal bacterial community. In the microbiome analysis, enriched genera abundance of Clostridium ASF356 and Coriobacteriaceae CHKCI002 was observed in birds fed the diet containing CV compared to those fed the control diet. Taken together, dietary CV supplementation might alter intestinal barrier function, immunity, and microbiomes in 10-day-old broiler chickens.

Multivalent Carbohydrate Nanocomposites for Tumor Microenvironment Remodeling to Enhance Antitumor Immunity.

Current cancer immunotherapeutic strategies mainly focus on remodeling the tumor microenvironment (TME) to make it favorable for antitumor immunity. Increasing attention has been paid to developing innovative immunomodulatory adjuvants that can restore weakened antitumor immunity by conferring immunogenicity to inflamed tumor tissues. Here, a galactan-enriched nanocomposite (Gal-NC) is developed from native carbohydrate structures through an optimized enzymatic transformation for effective, stable, and biosafe innate immunomodulation. Gal-NC is characterized as a carbohydrate nanoadjuvant with a macrophage-targeting feature. It is composed of repeating galactan glycopatterns derived from heteropolysaccharide structures of plant origin. The galactan repeats of Gal-NC function as multivalent pattern-recognition sites for Toll-like receptor 4 (TLR4). Functionally, Gal-NC-mediated TLR activation induces the repolarization of tumor-associated macrophages (TAMs) toward immunostimulatory/tumoricidal M1-like phenotypes. Gal-NC increases the intratumoral population of cytotoxic T cells, the main effector cells of antitumor immunity, via re-educated TAMs. These TME alterations synergistically enhance the T-cell-mediated antitumor response induced by αPD-1 administration, suggesting that Gal-NC has potential value as an adjuvant for immune checkpoint blockade combination therapies. Thus, the Gal-NC model established herein suggests a glycoengineering strategy to design a carbohydrate-based nanocomposite for advanced cancer immunotherapies.

Gauss curvature-based unique signatures of individual large earthquakes and its implications for customized data-driven prediction.

Statistical descriptions of earthquakes offer important probabilistic information, and newly emerging technologies of high-precision observations and machine learning collectively advance our knowledge regarding complex earthquake behaviors. Still, there remains a formidable knowledge gap for predicting individual large earthquakes' locations and magnitudes. Here, this study shows that the individual large earthquakes may have unique signatures that can be represented by new high-dimensional features-Gauss curvature-based coordinates. Particularly, the observed earthquake catalog data are transformed into a number of pseudo physics quantities (i.e., energy, power, vorticity, and Laplacian) which turn into smooth surface-like information via spatio-temporal convolution, giving rise to the new high-dimensional coordinates. Validations with 40-year earthquakes in the West U.S. region show that the new coordinates appear to hold uniqueness for individual large earthquakes ([Formula: see text]), and the pseudo physics quantities help identify a customized data-driven prediction model. A Bayesian evolutionary algorithm in conjunction with flexible bases can identify a data-driven model, demonstrating its promising reproduction of individual large earthquake's location and magnitude. Results imply that an individual large earthquake can be distinguished and remembered while its best-so-far model can be customized by machine learning. This study paves a new way to data-driven automated evolution of individual earthquake prediction.

Unraveling hidden rules behind the wet-to-dry transition of bubble array by glass-box physics rule learner.

A liquid-gas foam, here called bubble array, is a ubiquitous phenomenon widely observed in daily lives, food, pharmaceutical and cosmetic products, and even bio- and nano-technologies. This intriguing phenomenon has been often studied in a well-controlled environment in laboratories, computations, or analytical models. Still, real-world bubble undergoes complex nonlinear transitions from wet to dry conditions, which are hard to describe by unified rules as a whole. Here, we show that a few early-phase snapshots of bubble array can be learned by a glass-box physics rule learner (GPRL) leading to prediction rules of future bubble array. Unlike the black-box machine learning approach, the glass-box approach seeks to unravel expressive rules of the phenomenon that can evolve. Without known principles, GPRL identifies plausible rules of bubble prediction with an elongated bubble array data that transitions from wet to dry states. Then, the best-so-far GPRL-identified rule is applied to an independent circular bubble array, demonstrating the potential generality of the rule. We explain how GPRL uses the spatio-temporal convolved information of early bubbles to mimic the scientist's perception of bubble sides, shapes, and inter-bubble influences. This research will help combine foam physics and machine learning to better understand and control bubbles.

Curvature-Adjustable Polymeric Nanolens Fabrication Using UV-Controlled Nanoimprint Lithography.

Nanolenses are gaining importance in nanotechnology, but their challenging fabrication is thwarting their wider adoption. Of particular challenge is facile control of the lens' curvature. In this work, we demonstrate a new nanoimprinting technique capable of realizing polymeric nanolenses in which the nanolens' curvature is optically controlled by the ultraviolet (UV) dose at the pre-curing step. Our results reveal a regime in which the nanolens' height changes linearly with the UV dose. Computational modeling further uncovers that the polymer undergoes highly nonlinear dynamics during the UV-controlled nanoimprinting process. Both the technique and the process model will greatly advance nanoscale science and manufacturing technology.

A pilot study on the effect of formulation and individual muscle mass on vitamin C absorption in randomized clinical study.

The current study investigated that the vitamin C absorption in plasma depends on the individual muscle mass and the formulation including drinks (Vita 500), capsules, and tablets by using a randomized and double-blind clinical study. The volunteers were divided into two groups that depended on their muscle mass, including those whose muscle mass was greater than 40% ( ≥ $ \ge $ 40%) and less than 40% muscle mass (<40%). Levels of vitamin C in blood plasma was analyzed by high-performance liquid chromatography by ultraviolet detection (HPLC-UV). The existing HPLC method was modified according to lab conditions but maintained a constantly low pH sample reduction procedure. The analytical method validated stability, linearity, recovery, reliability, and accuracy. The vitamin C absorption was the highest at 120 min after ingesting Vita 500 (21.47 ± 15.99 µmol/L). It was higher in the group that has more than 40% muscle mass compared to other formulations, such as tablets and capsules. The results from the current study indicate that vitamin C formulations differently affect the vitamin C absorption, and its effect depends on the muscle mass. As the results, liquid type vitamin C formulations could enhance vitamin C absorption, which resulted in an improvement of vitamin C absorption according to muscle mass. PRACTICAL APPLICATION: The results of this study may recommend using vitamin C supplementation as liquid type. It may also provide evidence that people with higher muscle mass can absorb vitamin C more efficiently.

Mechano-Triboelectric Analysis of Surface Charge Generation on Replica-Molded Elastomeric Nanodomes.

Replica molding-based triboelectrification has emerged as a new and facile technique to generate nanopatterned tribocharge on elastomer surfaces. The "mechano-triboelectric charging model" has been developed to explain the mechanism of the charge formation and patterning process. However, this model has not been validated to cover the full variety of nanotexture shapes. Moreover, the experimental estimation of the tribocharge's surface density is still challenging due to the thick and insulating nature of the elastomeric substrate. In this work, we perform experiments in combination with numerical analysis to complete the mechano-triboelectrification charging model. By utilizing Kelvin probe force microscopy (KPFM) and finite element analysis, we reveal that the mechano-triboelectric charging model works for replica molding of both recessed and protruding nanotextures. In addition, by combining KPFM with numerical electrostatic modeling, we improve the accuracy of the surface charge density estimation and cross-calibrate the result against that of electrostatic force microscopy. Overall, the regions which underwent strong interfacial friction during the replica molding exhibited high surface potential and charge density, while those suffering from weak interfacial friction exhibited low values on both. These multi-physical approaches provide useful and important tools for comprehensive analysis of triboelectrification and generation of nanopatterned tribocharge. The results will widen our fundamental understanding of nanoscale triboelectricity and advance the nanopatterned charge generation process for future applications.

Rapid Design and Analysis of Microtube Pneumatic Actuators Using Line-Segment and Multi-Segment Euler-Bernoulli Beam Models.

Soft material-based pneumatic microtube actuators are attracting intense interest, since their bending motion is potentially useful for the safe manipulation of delicate biological objects. To increase their utility in biomedicine, researchers have begun to apply shape-engineering to the microtubes to diversify their bending patterns. However, design and analysis of such microtube actuators are challenging in general, due to their continuum natures and small dimensions. In this paper, we establish two methods for rapid design, analysis, and optimization of such complex, shape-engineered microtube actuators that are based on the line-segment model and the multi-segment Euler-Bernoulli's beam model, respectively, and are less computation-intensive than the more conventional method based on finite element analysis. To validate the models, we first realized multi-segment microtube actuators physically, then compared their experimentally observed motions against those obtained from the models. We obtained good agreements between the three sets of results with their maximum bending-angle errors falling within ±11%. In terms of computational efficiency, our models decreased the simulation time significantly, down to a few seconds, in contrast with the finite element analysis that sometimes can take hours. The models reported in this paper exhibit great potential for rapid and facile design and optimization of shape-engineered soft actuators.

Facile synthesis of NF/ZnOx and NF/CoOx nanostructures for high performance supercapacitor electrode materials.

NF/ZnOx nanocone and NF/CoOx nanoparticle electrode materials were fabricated on a nickel foam surface using a simple chemical bath deposition approach and assessed as an electrode material for high-performance supercapacitors (SCs). The electrochemical properties of the NF/ZnOx and NF/CoOx electrodes were examined by cyclic voltammetry, galvanostatic charge-discharge tests, and electrochemical impedance spectroscopy. The fabricated NF/ZnOx and NF/CoOx SCs devices exhibited a good specific capacitance of 2437 and 2142 F g-1 at a current density of 20 mA g-1, respectively, in a three electrode system. Furthermore, the NF/ZnOx and NF/CoOx electrode materials showed acceptable long cycle-life stability with 97.8% and 95.8% specific capacitance retention after 3000 cycles at a current density of 22 mA g-1 in a 2 M aqueous KOH solution. Furthermore, the NF/ZnOx and NF/CoOx SCs showed a high energy density of 54.15 W h kg-1 and 47.6 W h kg-1 at a power density of 499.8 W kg-1 and 571.2 W kg-1, respectively, with maximum operating voltage of 0.5 V. Overall, NF/ZnOx and NF/CoOx electrode materials are promising electrodes for electrochemical energy storage applications.

Exercise training acts as a therapeutic strategy for reduction of the pathogenic phenotypes for Alzheimer's disease in an NSE/APPsw-transgenic model.

Alzheimer's disease (AD) is a progressive neurodegenerative disease for which there are few therapeutic regimens that influence the underlying pathogenic phenotypes. However, of the currently available therapies, exercise training is considered to be one of the best candidates for amelioration of the pathological phenotypes of AD. Therefore, we directly investigated exercise training to determine whether it was able to ameliorate the molecular pathogenic phenotypes in the brain using a neuron-specific enolase (NSE)/Swedish mutation of amyloid precursor protein (APPsw) transgenic (Tg) mice as a novel AD model. To accomplish this, Non-Tg and NSE/ APPsw Tg mice were subjected to exercise on a treadmill for 16 weeks, after which their brains were evaluated to determine whether any changes in the pathological phenotype-related factors had occurred. The results indicated (i) that amyloid beta-42 (Abeta-42) peptides were significantly decreased in the NSE/APPsw Tg mice following exercise training; (ii) that exercise training inhibited the apoptotic biochemical cascades, including cytochrome c, caspase-9, caspase-3 and Bax; (iii) that the glucose transporter-1 (GLUT-1) and brain-derived neurotrophic factor (BDNF) proteins induced by exercise training protected the neurons from injury by inducing the concomitant expression of genes that encode proteins such as superoxide dismutase-1 (SOD-1), catalase and Bcl-2, which suppress oxidative stress and excitotoxic injury; (iv) that heat-shock protein-70 (HSP-70) and glucose-regulated protein-78 (GRP-78) were significantly increased in the exercise (EXE) group when compared to the sedentary (SED) group, and that these proteins may benefit the brain by making it more resistant to stress-induced neuron cell damage; (v) and that exercise training contributed to the restoration of normal levels of serum total cholesterol, insulin and glucose. Taken together, these results suggest that exercise training represents a practical therapeutic strategy for human subjects suffering from AD. Moreover, this training has the potential for use in new therapeutic strategies for the treatment of other chronic disease including diabetes, cardiovascular and Parkinson's disease.

Replica molding-based nanopatterning of tribocharge on elastomer with application to electrohydrodynamic nanolithography.

Replica molding often induces tribocharge on elastomers. To date, this phenomenon has been studied only on untextured elastomer surfaces even though replica molding is an effective method for their nanotexturing. Here we show that on elastomer surfaces nanotextured through replica molding the induced tribocharge also becomes patterned at nanoscale in close correlation with the nanotexture. By applying Kelvin probe microscopy, electrohydrodynamic lithography, and electrostatic analysis to our model nanostructure, poly(dimethylsiloxane) nanocup arrays replicated from a polycarbonate nanocone array, we reveal that the induced tribocharge is highly localized within the nanocup, especially around its rim. Through finite element analysis, we also find that the rim sustains the strongest friction during the demolding process. From these findings, we identify the demolding-induced friction as the main factor governing the tribocharge's nanoscale distribution pattern. By incorporating the resulting annular tribocharge into electrohydrodynamic lithography, we also accomplish facile realization of nanovolcanos with 10 nm-scale craters.