Flexible Symmetric-Defection Antenna with Bending and Thermal Insensitivity for Miniaturized UAV.

Flexible conformal-enabled antennas have great potential for various developable surface-built unmanned aerial vehicles (UAVs) due to their superior mechanical compliance as well as maintaining excellent electromagnetic features. However, it remains a challenge that the antenna holds bending and thermal insensitivity to negligibly shift resonant frequency during conformal attachment and aerial flight, respectively. Here, we report a flexible symmetric-defection antenna (FSDA) with bending and thermal insensitivity. By engraving a symmetric defection on the reflective ground, the radiated unit attached to the soft polydimethylsiloxane (PDMS) makes the antenna resonate at the ISM microwave band (resonant frequency = 2.44 GHz) and conformal with a miniaturized UAV. The antenna is also insensitive to both the bending-conformal attachment (20 mm < r < 70 mm) and thermal radiation (20~100 °C) due to the symmetric peripheral-current field along the defection and the low-change thermal effect of the PDMS, respectively. Therefore, the antenna in a non-bending state almost keeps the same impedance matching and radiation when it is attached to a cylinder-back of a UAV. The flexible antenna with bending and thermal insensitivity will pave the way for more conformal or wrapping applications.

A fully integrated, standalone stretchable device platform with in-sensor adaptive machine learning for rehabilitation.

Post-surgical treatments of the human throat often require continuous monitoring of diverse vital and muscle activities. However, wireless, continuous monitoring and analysis of these activities directly from the throat skin have not been developed. Here, we report the design and validation of a fully integrated standalone stretchable device platform that provides wireless measurements and machine learning-based analysis of diverse vibrations and muscle electrical activities from the throat. We demonstrate that the modified composite hydrogel with low contact impedance and reduced adhesion provides high-quality long-term monitoring of local muscle electrical signals. We show that the integrated triaxial broad-band accelerometer also measures large body movements and subtle physiological activities/vibrations. We find that the combined data processed by a 2D-like sequential feature extractor with fully connected neurons facilitates the classification of various motion/speech features at a high accuracy of over 90%, which adapts to the data with noise from motion artifacts or the data from new human subjects. The resulting standalone stretchable device with wireless monitoring and machine learning-based processing capabilities paves the way to design and apply wearable skin-interfaced systems for the remote monitoring and treatment evaluation of various diseases.

Identification of the BcLEA Gene Family and Functional Analysis of the BcLEA73 Gene in Wucai (Brassica campestris L.).

Late embryogenesis abundant (LEA) proteins are important developmental proteins in the response of plants to abiotic stress. In our previous study, BcLEA73 was differentially expressed under low-temperature stress. Herein, we combined bioinformatics analysis, subcellular localization, expression assays, and stress experiments (including salt, drought, and osmotic stress) to identify and analyze the BcLEA gene family. Gene cloning and functional analysis of BcLEA73 were performed in tobacco and Arabidopsis. Based on the sequence homology and the available conservative motif, 82 BrLEA gene family members were identified and were divided into eight subfamilies in the genome-wide database of Chinese cabbage. The analysis showed that the BrLEA73 gene was located on chromosome A09 and belonged to the LEA_6 subfamily. Quantitative real-time PCR analysis indicated that the BcLEA genes were differentially expressed to varying degrees in the roots, stems, leaves, and petioles of Wucai. The overexpressed BcLEA73 transgenic plants exhibited no significant differences in root length and seed germination rates compared to the wild-type (WT) plants under control conditions. Under salt and osmotic stress treatment, the root length and seed germination rates of the BcLEA73-OE strain were significantly greater than those of WT plants. Under salt stress, the total antioxidant capacity (T-AOC) of the BcLEA73-OE lines increased significantly, and the relative conductivity, (REL), hydrogen peroxide (H2O2) content, and superoxide anion (O2-) production rate decreased significantly. Under drought treatment, the survival rate of the BcLEA73-OE lines was significantly higher than that of WT plants. These results showed that the BcLEA73 gene of Wucai functions in enhancing the tolerance of plants to salt, drought, and osmotic stress. This study provides a theoretical basis to explore the relevant functions of the BcLEA gene family members of Wucai.

Attachable Inertial Device with Machine Learning toward Head Posture Monitoring in Attention Assessment.

The monitoring of head posture is crucial for interactive learning, in order to build feedback with learners' attention, especially in the explosion of digital teaching that occurred during the current COVID-19 pandemic. However, conventional monitoring based on computer vision remains a great challenge in the multi-freedom estimation of head posture, owing to low-angle annotation and limited training accuracy. Here, we report a fully integrated attachable inertial device (AID) that comfortably monitors in situ head posture at the neck, and provides a machine learning-based assessment of attention. The device consists of a stretchable inertial sensing unit and a fully integrated circuit-based system, as well as mechanically compliant encapsulation. Due to the mechanical flexibility, the device can be seamlessly attach to a human neck's epidermis without frequent user interactions, and wirelessly supports six-axial inertial measurements, thereby obtaining multidimensional tracking of individual posture. These head postures (40 types) are then divided into 10 rotation actions which correspond to diverse situations that usually occur in daily activities of teaching. Benefiting from a 2D convolutional neural network (CNN)-based machine learning model, their classification and prediction of head postures can be used to analyze and infer attention behavior. The results show that the proposed 2D CNN-based machine learning method can effectively distinguish the head motion posture, with a high accuracy of 98.00%, and three actual postures were successfully verified and evaluated in a predefined attention model. The inertial monitoring and attention evaluation based on attachable devices and machine learning will have potential in terms of learning feedback and planning for learners.

On-Skin Flexible Pressure Sensor with High Sensitivity for Portable Pulse Monitoring.

Radial artery pulse pressure contains abundant cardiovascular physiological and pathological information, which plays an important role in clinical diagnosis of traditional Chinese medical science. However, many photoelectric sensors and pressure sensors will lose a large number of waveform features in monitoring pulse, which will make it difficult for doctors to precisely evaluate the patients' health. In this letter, we proposed an on-skin flexible pressure sensor for monitoring radial artery pulse. The sensor consists of the MXene (Ti3C2Tx)-coated nonwoven fabrics (n-WFs) sensitive layer and laser-engraved interdigital copper electrodes. Benefiting from substantially increased conductive paths between fibers and electrodes during normal compression, the sensor obtains high sensitivity (3.187 kPa-1), fast response time (15 ms), low detection limit (11.1 Pa), and long-term durability (20,000 cycles). Furthermore, a flexible processing circuit was connected with the sensor mounted on wrist radial artery, achieving wirelessly precise monitoring of the pulse on smart phones in real time. Compared with the commercial flexible pressure sensor, our sensor successfully captures weak systolic peak precisely, showing its great clinical potential and commercial value.

Monolayer MoS2-Based Flexible and Highly Sensitive Pressure Sensor with Wide Sensing Range.

Flexible pressure sensors play an important role in flexible robotics, human-machine interaction (HMI), and human physiological information. However, most of the reported flexible pressure sensors suffer from a highly nonlinear response and a significant decrease in sensitivity at high pressures. Herein, we propose a flexible novel iontronic pressure sensor based on monolayer molybdenum disulfide (MoS2). Based on the unique structure and the excellent mechanical properties as well as the large intercalation capacitance of MoS2, the prepared sensor holds an ultra-high sensitivity (Smax = 89.75 kPa-1) and a wide sensing range (722.2 kPa). Further, the response time and relaxation time of the flexible sensor are only 3 ms, respectively, indicating that the device can respond to external pressure rapidly. In addition, it shows long-term cycling stability (over 5000 cycles with almost no degradation) at a high pressure of 138.9 kPa. Finally, it is demonstrated that the sensor can be used in physiological information monitoring and flexible robotics. It is anticipated that our prepared sensor provide a reliable approach to advance the theory and practicality of the flexible sensor electronics.

Stretchable and anti-impact iontronic pressure sensor with an ultrabroad linear range for biophysical monitoring and deep learning-aided knee rehabilitation.

Monitoring biophysical signals such as body or organ movements and other physical phenomena is necessary for patient rehabilitation. However, stretchable flexible pressure sensors with high sensitivity and a broad range that can meet these requirements are still lacking. Herein, we successfully monitored various vital biophysical features and implemented in-sensor dynamic deep learning for knee rehabilitation using an ultrabroad linear range and high-sensitivity stretchable iontronic pressure sensor (SIPS). We optimized the topological structure and material composition of the electrode to build a fully stretching on-skin sensor. The high sensitivity (12.43 kPa-1), ultrabroad linear sensing range (1 MPa), high pressure resolution (6.4 Pa), long-term durability (no decay after 12000 cycles), and excellent stretchability (up to 20%) allow the sensor to maintain operating stability, even in emergency cases with a high sudden impact force (near 1 MPa) applied to the sensor. As a practical demonstration, the SIPS can positively track biophysical signals such as pulse waves, muscle movements, and plantar pressure. Importantly, with the help of a neuro-inspired fully convolutional network algorithm, the SIPS can accurately predict knee joint postures for better rehabilitation after orthopedic surgery. Our SIPS has potential as a promising candidate for wearable electronics and artificial intelligent medical engineering owing to its unique high signal-to-noise ratio and ultrabroad linear range. An ultrabroad-linear range (1 MPa) iontronic pressure sensor with superior sensitivity (12.43 kPa-1) and stretchability (up to 20%) was proposed for biophysical monitoring and deep learning-based knee-rehabilitation training.

Highly Sensitive Pseudocapacitive Iontronic Pressure Sensor with Broad Sensing Range.

HIGHLIGHTS: The iontronic pressure sensor achieved an ultrahigh sensitivity (Smin > 200 kPa-1, Smax > 45,000 kPa-1). The iontronic pressure sensor exhibited a broad sensing range of over 1.4 MPa. Pseudocapacitive iontronic pressure sensor using MXene was proposed. Flexible pressure sensors are unprecedentedly studied on monitoring human physical activities and robotics. Simultaneously, improving the response sensitivity and sensing range of flexible pressure sensors is a great challenge, which hinders the devices' practical application. Targeting this obstacle, we developed a Ti3C2Tx-derived iontronic pressure sensor (TIPS) by taking the advantages of the high intercalation pseudocapacitance under high pressure and rationally designed structural configuration. TIPS achieved an ultrahigh sensitivity (Smin > 200 kPa-1, Smax > 45,000 kPa-1) in a broad sensing range of over 1.4 MPa and low limit of detection of 20 Pa as well as stable long-term working durability for 10,000 cycles. The practical application of TIPS in physical activity monitoring and flexible robot manifested its versatile potential. This study provides a demonstration for exploring pseudocapacitive materials for building flexible iontronic sensors with ultrahigh sensitivity and sensing range to advance the development of high-performance wearable electronics.

Pathways of amyloid-beta absorption and aggregation in a membranous environment.

Aggregation of misfolded oligomeric amyloid-beta (Aß) peptides on lipid membranes has been identified as a primary event in Alzheimer's pathogenesis. However, the structural and dynamical features of this membrane assisted Aß aggregation have not been well characterized. The microscopic characterization of dynamic molecular-level interactions in peptide aggregation pathways has been challenging both computationally and experimentally. In this work, we explore differential patterns of membrane-induced Aß 16-22 (K-L-V-F-F-A-E) aggregation from the microscopic perspective of molecular interactions. Physics-based coarse-grained molecular dynamics (CG-MD) simulations were employed to investigate the effect of lipid headgroup charge - zwitterionic (1-palmitoyl-2-oleoyl-sn-glycero-3-phosphocholine: POPC) and anionic (1-palmitoyl-2-oleoyl-sn-glycero-3-phospho-l-serine: POPS) - on Aß 16-22 peptide aggregation. Our analyses present an extensive overview of multiple pathways for peptide absorption and biomechanical forces governing peptide folding and aggregation. In agreement with experimental observations, anionic POPS molecules promote extended configurations in Aß peptides that contribute towards faster emergence of ordered ß-sheet-rich peptide assemblies compared to POPC, suggesting faster fibrillation. In addition, lower cumulative rates of peptide aggregation in POPS due to higher peptide-lipid interactions and slower lipid diffusion result in multiple distinct ordered peptide aggregates that can serve as nucleation seeds for subsequent Aß aggregation. This study provides an in-silico assessment of experimentally observed aggregation patterns, presents new morphological insights and highlights the importance of lipid headgroup chemistry in modulating the peptide absorption and aggregation process.

Mesoporous Encapsulated Chiral Nanogold for Use in Enantioselective Reactions.

Although various nanomaterials have been designed for biocatalysis, few of them can accelerate chemical reactions with high selectivity and stereocontrol, which remains them from being perfect alternatives to nature enzymes. Herein, inspired by the natural enzymes, an enantioselective nanomaterial has been constructed, with gold nanoparticles (AuNPs) as active centers, chiral cysteine (Cys) as selectors for chiral recognition, and expanded mesoporous silica (EMSN) as a skeleton of the artificial enzyme. In the oxidation of chiral 3,4-dihydroxy-phenylalanine (DOPA), the nanozyme with d-Cys shows preference to l-DOPA while the artificial enzyme with l-Cys shows preference to d-DOPA. Subsequent calculation of apparent steady-state kinetic parameters and activation energies together with molecular dynamics (MD) simulations showed that the different affinity precipitated by hydrogen bonding formation between chiral Cys and DOPA is the origin of chiral selectivity.

How Hydrophobic Hydration Destabilizes Surfactant Micelles at Low Temperature: A Coarse-Grained Simulation Study.

Micelles are self-assembled aggregates of amphiphilic surfactant molecules that are important in a variety of applications, including drug delivery, detergency, and catalysis. It is known that the micellization process is driven by the same physiochemical forces that drive protein folding, aggregation, and biological membrane self-assembly. Nevertheless, the molecular details of how micelle stability changes in water at low temperature are not fully clear. We develop and use a coarse-grained model to investigate how the interplay between nonionic surfactants and the surrounding water at the nanoscale affects the stability of micelles at high and low temperatures. Simulations of preformed C12E5 micelles in explicit water at a range of temperatures reveal the existence of two distinct surfactant conformations within the micelle, a bent structure and an extended structure, the latter being more prevalent at low temperature. Favorable interactions of the surfactant with more ordered solvation water stabilizes the extended configuration, allowing nanoscale wetting of the dry, hydrophobic core of the micelle, leading to the micelle breaking. Taken together, our coarse-grained simulations unravel how energetic and structural changes of the surfactant and the surrounding water destabilize micelles at low temperature, which is a direct consequence of the weakened hydrophobicity. Our approach thus provides an effective mean for extracting the molecular-level changes during hydrophobicity-driven destabilization of molecular self-assembly, which is important in a wide range of fields, including biology, polymer science, and nanotechnology.

Effect of pH on chitosan hydrogel polymer network structure.

Chitosan is a molecule that can form water-filled 3D polymer networks with a wide range of applications. A new coarse-grained model for chitosan hydrogel was developed to explore its pH-dependent self-assembly behavior and mechanical properties. Our results indicate that the underlying polymer physical crosslinking pattern induced by solution pH has a significant effect on hydrogel elastic moduli. With this model, we obtain pH-dependent structural and mechanical property changes in agreement with experimental observations, and provide a molecular mechanism behind the changes in polymer crosslinking patterns.

Influence of Monovalent Cation Size on Nanodomain Formation in Anionic-Zwitterionic Mixed Bilayers.

Phosphatidylserine (PS) and phosphatidylcholine (PC) are two of the major anionic and zwitterionic phospholipids in mammalian cell membranes. Ion-PS interaction is hypothesized to play a crucial role in a range of biological events including membrane fusion, lipid phase modulation, membrane protein insertion and translocation. In this study, we characterize lipid nanodomain formation in PC/PS mixed bilayers using coarse-grained simulations. We investigate the role of monovalent cation sizes in modulating lipid-ion binding modes and lipid demixing. Our simulations suggest that certain lipid-ion binding modes lead to growth of ion-mediated PS lipid clusters. The existing literature reveals the polymorphism in binding and partitioning patterns in monovalent cations (Na+, K+, and Li+) with anionic lipids. Our work provides a microscopic view on the ion-size-dependent PS lipid packing pattern observed experimentally. A coupled relationship between lipid curvature and asymmetry is observed in highly demixed PC/PS mixed bilayers.

Correction: Effect of lipid head group interactions on membrane properties and membrane-induced cationic ß-hairpin folding.

Correction for 'Effect of lipid head group interactions on membrane properties and membrane-induced cationic ß-hairpin folding' by Sai J. Ganesan et al., Phys. Chem. Chem. Phys., 2016, 18, 17836-17850.

Effect of lipid head group interactions on membrane properties and membrane-induced cationic ß-hairpin folding.

Membrane interfaces (mIFs) are ubiquitous components of living cells and are host to many essential biological processes. One key characteristic of mIFs is the dielectric gradient and, subsequently, electrostatic potential that arises from dipolar interactions in the head group region. In this work, we present a coarse-grained (CG) model for anionic and zwitterionic lipids that accounts for dipolar intricacies in the head group region. Prior work on adding dipolar interactions in a coarse grained (CG) model for peptides enabled us to achieve a/b secondary structure content de novo, without any added bias. We have now extended this idea to lipids. To mimic dipolar interactions, two dummy particles with opposite charges are added to CG polar beads. These two dummy charges represent a fluctuating dipole that introduces structural polarization into the head group region. We have used POPC (1-palmitoyl-2-oleoyl-sn-glycero-3-phosphocholine) and POPS (1-palmitoyl-2-oleoyl-sn-glycero-3-phospho-l-serine) as our model lipids. We characterize structural, dynamic, and dielectric properties of our CG bilayer, along with the effect of monovalent ions. We observe head group dipoles to play a significant role in membrane dielectric gradient and lipid clustering induced by dipole­dipole interactions in POPS lipids. In addition, we have studied membrane-induced peptide folding of a cationic antimicrobial peptide with anticancer activity, SVS-1. We find that membrane-induced peptide folding is driven by both (a) cooperativity in peptide self-interaction and (b) cooperativity in membrane­peptide interaction. In particular, dipolar interactions between the peptide backbone and lipid head groups contribute to stabilizing folded conformations [corrected].