Nonparametric Modeling and Prognosis of Condition Monitoring Signals Using Multivariate Gaussian Convolution Processes

<p>Condition monitoring (CM) signals play a critical role in assessing the remaining useful life of in-service components. In this article, an alternative view on modeling CM signals is proposed. This view draws its roots from multitask learning and is based on treating each CM signal as an individual task. Each task is then expressed as a convolution of a latent function drawn from a Gaussian process (GP), and the transfer of knowledge is achieved through sharing these latent functions between historical and in-service CM signals. Aside from being nonparametric, the flexible and individualistic approach in our model can account for heterogeneity in the data and automatically infer the commonalities between the new testing observations and CM signals in the historical dataset. The robustness and advantageous features of the proposed method are demonstrated through numerical studies and a case study with real-world data in the application to find the remaining useful life prediction of automotive lead-acid batteries. Technical details and additional numerical results are available in the supplementary materials.</p

Retraining and Optimizing DNA-Hydrolyzing Deoxyribozymes for Robust Single- and Multiple-Turnover Activities

Recently, we reported two classes of Zn²⁺-dependent DNA-hydrolyzing deoxyribozymes. The class I deoxyribozymes can adopt a secondary structure of either hairpin or stem-loop-stem. The corresponding most active representatives, I-R1 and I-R3, exhibit single-turnover <i>k</i>_obs values of ∼0.059 and ∼1.0 min^–1 at 37 °C, respectively. Further analysis revealed that I-R3 could perform slow multiple-turnover catalysis with a <i>k</i>_cat of ∼0.017 min^–1 at 37 °C. In this study, we sought to retrain and optimize the class I deoxyribozymes for robust single- and multiple-turnover cleavage activities. Refined consensus sequences were derived based on the data of <i>in vitro</i> reselection from the degenerate DNA pools. By examining individual candidates, we obtained the I-R1 mutants I-R1a-c with improved single-turnover <i>k</i>_obs values of 0.68–0.76 min^–1 at 37 °C, over 10 times faster than I-R1. Meanwhile, we further demonstrated that I-R1a–c and I-R3 are thermophilic. As temperature went higher beyond 45 °C, I-R3 cleaved faster with the <i>k</i>_obs value reaching its maximum of ∼3.5 min^–1 at 54 °C. Using a series of the <i>k</i>_obs values of I-R3 from 37 to 54 °C, we calculated the apparent activation energy <i>E</i>_a to be ∼15 ± 3 kcal/mol for the DNA-catalyzed hydrolysis of DNA phosphodiester bond. In addition, we were able to design a simple yet efficient thermal-cycling protocol to boost the effective <i>k</i>_cat of I-R3 from 0.017 to 0.50 min^–1, which corresponds to an ∼30-fold improvement of the multiple-turnover activity. The data and findings provide insights on the enzymatic robustness of DNA-catalyzed DNA hydrolysis and offer general strategies to study various DNA enzymes

Short‑, Medium‑, and Long-Chain Chlorinated Paraffins in Wildlife from Paddy Fields in the Yangtze River Delta

Short-chain chlorinated paraffins (SCCPs) were added to Annex A of the Stockholm Convention on Persistent Organic Pollutants in April, 2017. As a consequence of this regulation, increasing production and usage of alternatives, such as medium- and long-chain chlorinated paraffins (MCCPs and LCCPs, respectively), is expected. Little is known about the environmental fate and behavior of MCCPs and LCCPs. In the present study, SCCPs, MCCPs, and LCCPs were analyzed in nine wildlife species from paddy fields in the Yangtze River Delta, China, using atmospheric pressure chemical ionization-quadrupole time-of-flight mass spectrometry. SCCPs, MCCPs, and LCCPs were detected in all samples at concentrations ranging from <91–43 000, 96–33 000, and 14–10 000 ng/g lipid, respectively. Most species contained primarily MCCPs (on average 44%), with the exception of collared scops owl and common cuckoo, in which SCCPs (43%) accumulated to a significantly (i.e., <i>p</i> < 0.05) greater extent than MCCPs (40%). Cl₆ groups were dominant in most species except for yellow weasel and short-tailed mamushi, which contained primarily Cl₇ groups. Principal components analysis, together with CP concentrations and carbon stable isotope analysis showed that habitat and feeding habits were key factors driving CP accumulation and congener group patterns in wildlife. This is the first report of LCCP exposure in wildlife and highlights the need for data on risks associated with CP usage

Lithium-Ion Batteries: Charged by Triboelectric Nanogenerators with Pulsed Output Based on the Enhanced Cycling Stability

The triboelectric nanogenerator (TENG) has been used to store its generated energy into lithium-ion batteries (LIBs); however, the influences of its pulse current and high voltage on LIB polarization and dynamic behaviors have not been investigated yet. In this paper, it is found that LIBs based on the phase transition reaction of the lithium storage mechanism [LiFePO₄ (LFP) and Li₄Ti₅O₁₂ (LTO) electrodes] are more suitable for charging by TENGs. Thus, the enhanced cycling capacity, Coulombic efficiency (nearly 100% for LTO electrode), and energy storage efficiency (85.3% for the LFP–LTO electrode) are successfully achieved. Moreover, the pulse current has a positive effect on the increase of the Li-ion extraction, reducing the charge-transfer resistance (<i>R</i>_ct) for all studied electrodes as well (LFP, LiNi_0.6Co_0.2Mn_0.2O₂, LTO, and graphite). The excellent cyclability, high Coulombic, and energy storage efficiencies demonstrated the availability of storing pulsed energy generated by TENGs. This research has provided a promising analysis to obtain an enhanced charging methodology, which provides significant guidance for the scientific research of the LIBs

Screen-Printed Washable Electronic Textiles as Self-Powered Touch/Gesture Tribo-Sensors for Intelligent Human–Machine Interaction

Multifunctional electronic textiles (E-textiles) with embedded electric circuits hold great application prospects for future wearable electronics. However, most E-textiles still have critical challenges, including air permeability, satisfactory washability, and mass fabrication. In this work, we fabricate a washable E-textile that addresses all of the concerns and shows its application as a self-powered triboelectric gesture textile for intelligent human–machine interfacing. Utilizing conductive carbon nanotubes (CNTs) and screen-printing technology, this kind of E-textile embraces high conductivity (0.2 kΩ/sq), high air permeability (88.2 mm/s), and can be manufactured on common fabric at large scales. Due to the advantage of the interaction between the CNTs and the fabrics, the electrode shows excellent stability under harsh mechanical deformation and even after being washed. Moreover, based on a single-electrode mode triboelectric nanogenerator and electrode pattern design, our E-textile exhibits highly sensitive touch/gesture sensing performance and has potential applications for human–machine interfacing

Screen-Printed Washable Electronic Textiles as Self-Powered Touch/Gesture Tribo-Sensors for Intelligent Human–Machine Interaction

Multifunctional electronic textiles (E-textiles) with embedded electric circuits hold great application prospects for future wearable electronics. However, most E-textiles still have critical challenges, including air permeability, satisfactory washability, and mass fabrication. In this work, we fabricate a washable E-textile that addresses all of the concerns and shows its application as a self-powered triboelectric gesture textile for intelligent human–machine interfacing. Utilizing conductive carbon nanotubes (CNTs) and screen-printing technology, this kind of E-textile embraces high conductivity (0.2 kΩ/sq), high air permeability (88.2 mm/s), and can be manufactured on common fabric at large scales. Due to the advantage of the interaction between the CNTs and the fabrics, the electrode shows excellent stability under harsh mechanical deformation and even after being washed. Moreover, based on a single-electrode mode triboelectric nanogenerator and electrode pattern design, our E-textile exhibits highly sensitive touch/gesture sensing performance and has potential applications for human–machine interfacing

Screen-Printed Washable Electronic Textiles as Self-Powered Touch/Gesture Tribo-Sensors for Intelligent Human–Machine Interaction

Multifunctional electronic textiles (E-textiles) with embedded electric circuits hold great application prospects for future wearable electronics. However, most E-textiles still have critical challenges, including air permeability, satisfactory washability, and mass fabrication. In this work, we fabricate a washable E-textile that addresses all of the concerns and shows its application as a self-powered triboelectric gesture textile for intelligent human–machine interfacing. Utilizing conductive carbon nanotubes (CNTs) and screen-printing technology, this kind of E-textile embraces high conductivity (0.2 kΩ/sq), high air permeability (88.2 mm/s), and can be manufactured on common fabric at large scales. Due to the advantage of the interaction between the CNTs and the fabrics, the electrode shows excellent stability under harsh mechanical deformation and even after being washed. Moreover, based on a single-electrode mode triboelectric nanogenerator and electrode pattern design, our E-textile exhibits highly sensitive touch/gesture sensing performance and has potential applications for human–machine interfacing

All-Nanofiber-Based Ultralight Stretchable Triboelectric Nanogenerator for Self-Powered Wearable Electronics

The flexible and stretchable electronics have been considered as next-generation electronics. Stretchable triboelectric nanogenerators (S-TENGs) with both multifunction and comfort have become a hot field of research for wearable electronic devices recently. Here, we designed an all-nanofiber-based, ultralight, S-TENG that could be softly attached on skins for motion energy harvesting and self-powered biomechanical monitoring. The S-TENG consisted of only two nanofiber membranes: a polyvinylidene fluoride nanofiber membrane (PVDFNM) supported by thermoplastic polyurethane nanofiber membrane (TPUNM) was used as the frictional layer, and a multiwalled carbon nanotube (MWCNT) conductive material screen-printed on the TPUNM was used as the electrode layer. Due to the excellent stretchability of TPUNM, the S-TENG could generate electricity under various types of deformation, and regains its original performance after intense mechanical extension, even if it is partially cut or damaged. Owing to the great electronegativity of PVDFNM, the device generated a maximum voltage of 225 V and a current of 4.5 μA with an electrode area of 6 × 1 cm². The S-TENG has great potential applications in self-powered wearable devices, electronic skins, and smart sensor networks

Screen-Printed Washable Electronic Textiles as Self-Powered Touch/Gesture Tribo-Sensors for Intelligent Human–Machine Interaction

Multifunctional electronic textiles (E-textiles) with embedded electric circuits hold great application prospects for future wearable electronics. However, most E-textiles still have critical challenges, including air permeability, satisfactory washability, and mass fabrication. In this work, we fabricate a washable E-textile that addresses all of the concerns and shows its application as a self-powered triboelectric gesture textile for intelligent human–machine interfacing. Utilizing conductive carbon nanotubes (CNTs) and screen-printing technology, this kind of E-textile embraces high conductivity (0.2 kΩ/sq), high air permeability (88.2 mm/s), and can be manufactured on common fabric at large scales. Due to the advantage of the interaction between the CNTs and the fabrics, the electrode shows excellent stability under harsh mechanical deformation and even after being washed. Moreover, based on a single-electrode mode triboelectric nanogenerator and electrode pattern design, our E-textile exhibits highly sensitive touch/gesture sensing performance and has potential applications for human–machine interfacing

Screen-Printed Washable Electronic Textiles as Self-Powered Touch/Gesture Tribo-Sensors for Intelligent Human–Machine Interaction

Multifunctional electronic textiles (E-textiles) with embedded electric circuits hold great application prospects for future wearable electronics. However, most E-textiles still have critical challenges, including air permeability, satisfactory washability, and mass fabrication. In this work, we fabricate a washable E-textile that addresses all of the concerns and shows its application as a self-powered triboelectric gesture textile for intelligent human–machine interfacing. Utilizing conductive carbon nanotubes (CNTs) and screen-printing technology, this kind of E-textile embraces high conductivity (0.2 kΩ/sq), high air permeability (88.2 mm/s), and can be manufactured on common fabric at large scales. Due to the advantage of the interaction between the CNTs and the fabrics, the electrode shows excellent stability under harsh mechanical deformation and even after being washed. Moreover, based on a single-electrode mode triboelectric nanogenerator and electrode pattern design, our E-textile exhibits highly sensitive touch/gesture sensing performance and has potential applications for human–machine interfacing