Nanogenerator-based self-powered sensors for data collection

Self-powered sensors can provide energy and environmental data for applications regarding the Internet of Things, big data, and artificial intelligence. Nanogenerators provide excellent material compatibility, which also leads to a rich variety of nanogenerator-based self-powered sensors. This article reviews the development of nanogenerator-based self-powered sensors for the collection of human physiological data and external environmental data. Nanogenerator-based self-powered sensors can be designed to detect physiological data as wearable and implantable devices. Nanogenerator-based self-powered sensors are a solution for collecting data and expanding data dimensions in a future intelligent society. The future key challenges and potential solutions regarding nanogenerator-based self-powered sensors are discussed

LIN28 Is Involved in Glioma Carcinogenesis and Predicts Outcomes of Glioblastoma Multiforme Patients

LIN28, an evolutionarily conversed RNA binding protein which can bind to the terminal loops of let-7 family microRNA precursors and block their processing to maturation, is highly expressed in several subsets of tumors that carry poor prognoses, such as ovarian carcinoma, hepatocellular carcinoma, colon carcinoma and germ cell carcinoma. However, there has been no study on the expression of LIN28 in glioma tissues or their importance as a prognostic predictor of glioma patients. This study aimed to examine the expression of LIN28 in glioma and correlate the results to patient outcome. We found that LIN28 expression was significantly higher in the group of patients with a poor prognosis compared to patients with a good prognosis by gene microarray. Log-rank analysis showed patients with higher LIN28 expression level in tumor had a shorter progression-free survival and overall survival times compared to those with lower LIN28 expression level. Similar results were also obtained from the tissue microarray analysis. Univariate and multivariate analyses showed high LIN28 expression was an independent prognostic factor for a shorter progression-free survival and overall survival in GBM patients. Furthermore in vitro experiments showed that down-regulation of LIN28 in U251 and U373 cells caused cell cycle arrest in the G1 phase, delayed cell proliferation, increased apoptosis, and resulted in fewer colonies compared to controls. Summarily, our data provides a potential target for cancer therapy as an approach to overcome the poor options currently available for GBM patients

MAX-DOAS measurements of tropospheric NO2 and HCHO in Nanjing and a comparison to ozone monitoring instrument observations

In this paper, we present long-term observations of atmospheric nitrogen dioxide (NO2) and formaldehyde (HCHO) in Nanjing using a Multi-AXis Differential Optical Absorption Spectroscopy (MAX-DOAS) instrument. Ground-based MAX-DOAS measurements were performed from April 2013 to February 2017. The MAX-DOAS measurements of NO2 and HCHO vertical column densities (VCDs) are used to validate ozone monitoring instrument (OMI) satellite observations over Nanjing. The comparison shows that the OMI observations of NO2 correlate well with the MAX-DOAS data with Pearson correlation coefficient (R) of 0.91. However, OMI observations are on average a factor of 3 lower than the MAX-DOAS measurements. Replacing the a priori NO2 profiles by the MAX-DOAS profiles in the OMI NO2 VCD retrieval would increase the OMI NO2 VCDs by similar to 30% with correlation nearly unchanged. The comparison result of MAX-DOAS and OMI observations of HCHO VCD shows a good agreement with R of 0.75 and the slope of the regression line is 0.99. An age-weighted backward-propagation approach is applied to the MAX-DOAS measurements of NO2 and HCHO to reconstruct the spatial distribution of NO2 and HCHO over the Yangtze River Delta during summer and winter time. The reconstructed NO2 fields show a distinct agreement with OMI satellite observations. However, due to the short atmospheric lifetime of HCHO, the backward-propagated HCHO data do not show a strong spatial correlation with the OMI HCHO observations. The result shows that the MAX-DOAS measurements are sensitive to the air pollution transportation in the Yangtze River Delta, indicating the air quality in Nanjing is significantly influenced by regional transportation of air pollutants. The MAX-DOAS data are also used to evaluate the effectiveness of air pollution control measures implemented during the Youth Olympic Games 2014. The MAX-DOAS data show a significant reduction of ambient aerosol, NO2 and HCHO (30 %-50 %) during the Youth Olympic Games. Our results provide a better understanding of the transportation and sources of pollutants over the Yangtze River Delta as well as the effect of emission control measures during large international events, which are important for the future design of air pollution control policies

On the mode-segregated aerosol particle number concentration load : contributions of primary and secondary particles in Hyytiälä and Nanjing

Aerosol particle concentrations in the atmosphere are governed by their sources and sinks. Sources include directly-emitted (primary) and secondary aerosol particles formed from gas-phase precursor compounds. The relative importance of primary and secondary aerosol particles varies regionally and with time. In this work, we investigated primary and secondary contributions to mode-segregated particle number concentrations by using black carbon as a tracer for the primary aerosol number concentration. We studied separately nucleation, Aitken and accumulation mode concentrations at a rural boreal forest site (Hyytiala, Finland) and in a rather polluted megacity environment (Nanjing, China) using observational data from 2011 to 2014. In both places and in all the modes, the majority of particles were estimated to be of secondary origin. Even in Nanjing, only about half of the accumulation mode particles were estimated to be of primary origin. Secondary particles dominated particularly in the nucleation and Aitken modes.Peer reviewe

Observations of aerosol optical properties at a coastal site in Hong Kong, South China

Temporal variations in aerosol optical properties were investigated at a coastal station in Hong Kong based on the field observation from February 2012 to February 2015. At 550 nm, the average light-scattering (151 +/- 100Mm(-1) / and absorption coefficients (8.3 +/- 6.1Mm(-1) / were lower than most of other rural sites in eastern China, while the single-scattering albedo (SSA = 0.93 +/- 0.05) was relatively higher compared with other rural sites in the Pearl River Delta (PRD) region. Correlation analysis confirmed that the darkest aerosols were smaller in particle size and showed strong scattering wavelength dependencies, indicating possible sources from fresh emissions close to the measurement site. Particles with D-p of 200-800 nm were less in number, yet contributed the most to the light-scattering coefficients among submicron particles. In summer, both Delta BC / Delta CO and SO2 / BC peaked, indicating the impact of nearby combustion sources on this site. Multi-year backward Lagrangian particle dispersion modeling (LPDM) and potential source contribution (PSC) analysis revealed that these particles were mainly from the air masses that moved southward over Shenzhen and urban Hong Kong and the polluted marine air containing ship exhausts. These fresh emission sources led to low SSA during summer months. For winter and autumn months, contrarily, Delta BC / Delta CO and SO2 / BC were relatively low, showing that the site was more under influence of well-mixed air masses from long-range transport including from South China, East China coastal regions, and aged aerosol transported over the Pacific Ocean and Taiwan, causing stronger abilities of light extinction and larger variability of aerosol optical properties. Our results showed that ship emissions in the vicinity of Hong Kong could have visible impact on the light-scattering and absorption abilities as well as SSA at Hok Tsui.Peer reviewe

Estimating cloud condensation nuclei number concentrations using aerosol optical properties : role of particle number size distribution and parameterization

The concentration of cloud condensation nuclei (CCN) is an essential parameter affecting aerosol–cloud interactions within warm clouds. Long-term CCN number concentration (NCCN) data are scarce; there are a lot more data on aerosol optical properties (AOPs). It is therefore valuable to derive parameterizations for estimating NCCN from AOP measurements. Such parameterizations have already been made, and in the present work a new parameterization is presented. The relationships between NCCN, AOPs, and size distributions were investigated based on in situ measurement data from six stations in very different environments around the world. The relationships were used for deriving a parameterization that depends on the scattering Ångström exponent (SAE), backscatter fraction (BSF), and total scattering coefficient (σsp) of PM10 particles. The analysis first showed that the dependence of NCCN on supersaturation (SS) can be described by a logarithmic fit in the range SS 4. At SS >0.4 % the average bias ranged from ∼0.7 to ∼1.3 at most sites. For the marine-aerosol-dominated site Ascension Island the bias was higher, ∼1.4–1.9. In other words, at SS >0.4 % NCCN was estimated with an average uncertainty of approximately 30 % by using nephelometer data. The biases were mainly due to the biases in the parameterization related to the scattering Ångström exponent (SAE). The squared correlation coefficients between the AOP-derived and measured NCCN varied from ∼0.5 to ∼0.8. To study the physical explanation of the relationships between NCCN and AOPs, lognormal unimodal particle size distributions were generated and NCCN and AOPs were calculated. The simulation showed that the relationships of NCCN and AOPs are affected by the geometric mean diameter and width of the size distribution and the activation diameter. The relationships of NCCN and AOPs were similar to those of the observed ones.The concentration of cloud condensation nuclei (CCN) is an essential parameter affecting aerosol-cloud interactions within warm clouds. Long-term CCN number concentration (N-CCN) data are scarce; there are a lot more data on aerosol optical properties (AOPs). It is therefore valuable to derive parameterizations for estimating N-CCN from AOP measurements. Such parameterizations have already been made, and in the present work a new parameterization is presented. The relationships between N-CCN, AOPs, and size distributions were investigated based on in situ measurement data from six stations in very different environments around the world. The relationships were used for deriving a parameterization that depends on the scattering Angstrom exponent (SAE), backscatter fraction (BSF), and total scattering coefficient (sigma(sp)) of PM10 particles. The analysis first showed that the dependence of N-CCN on supersaturation (SS) can be described by a logarithmic fit in the range SS 4. At SS > 0 :4% the average bias ranged from similar to 0.7 to similar to 1.3 at most sites. For the marine-aerosol-dominated site Ascension Island the bias was higher, similar to 1.4-1.9. In other words, at SS > 0:4% N-CCN was estimated with an average uncertainty of approximately 30% by using nephelometer data. The biases were mainly due to the biases in the parameterization related to the scattering Angstrom exponent (SAE). The squared correlation coefficients between the AOP-derived and measured N-CCN varied from similar to 0.5 to similar to 0.8. To study the physical explanation of the relationships between N-CCN and AOPs, lognormal unimodal particle size distributions were generated and N-CCN and AOPs were calculated. The simulation showed that the relationships of N-CCN and AOPs are affected by the geometric mean diameter and width of the size distribution and the activation diameter. The relationships of N-CCN and AOPs were similar to those of the observed ones.Peer reviewe

Cluster Analysis of Submicron Particle Number Size Distributions at the SORPES Station in the Yangtze River Delta of East China

Submicron particles in polluted regions have received much attention because of their influences on human health and climate. A k-means clustering technique was performed on a data set of particle number size distributions (PNSD) that was obtained over more than 3 years in the Yangtze River Delta (YRD) region of East China. With simultaneous measurements of meteorological conditions, trace gases and aerosol compositions, seven clusters were categorized and interpreted. Cluster 1 and cluster 2, which accounted for 9.9% of the total PNSD data, were attributed to new particle formation (NPF) and vehicle exhaust emissions with different intensities; Cluster 3 and Cluster 4, which accounted for 10.5% of the total PNSD data, were related to the growth of nucleation mode particles; Cluster 5, which accounted for 37.9% of the total data, was attributed to the humid YRD background; and Cluster 6 and Cluster 7, which accounted for 41.6% of the total data set, were both pollution-related clusters with similar mass concentrations but completely different PNSD. Although the PM2.5 mass concentrations were somewhat similar, the particle number concentrations of the accumulation mode particles could vary by more than one order of magnitude from the urban background cluster to the pollution-related clusters. The cluster proximity diagram and conversion flow chart of clusters clearly show the influence of NPF and growth on haze, as well as the conversion between background and polluted conditions. This study highlights the importance of PNSD for understanding urban air quality and recommends the clustering technique for analyzing complex PNSD datasets. Plain Language Summary Submicron particles in polluted regions have significant influences on human health and climate. Based on long-term field measurements, we used the k-means clustering technique to characterize the number size distributions of submicron particles in the Yangtze River Delta (YRD) of China. Seven clusters were categorized and interpreted. New particle formation (NPF), fossil fuel combustion and biomass burning are the main sources of submicron particles in the YRD. The influences of NPF and growth on haze, as well as the conversion between background and polluted conditions, were found. Key Points New particle formation (NPF), fossil fuel combustion and biomass burning are the main sources of submicron particles in Nanjing The influences of NPF and growth on haze, and the conversion between background and pollution conditions were found The k-means cluster technique is an effective tool to categorize particle number size distribution data setPeer reviewe

Understanding and modelling wildfire regimes: An ecological perspective

© 2021 The Author(s).Recent extreme wildfire seasons in several regions have been associated with exceptionally hot, dry conditions, made more probable by climate change. Much research has focused on extreme fire weather and its drivers, but natural wildfire regimes—and their interactions with human activities—are far from being comprehensively understood. There is a lack of clarity about the 'causes' of wildfire, and about how ecosystems could be managed for the co-existence of wildfire and people. We present evidence supporting an ecosystem-centred framework for improved understanding and modelling of wildfire. Wildfire has a long geological history and is a pervasive natural process in contemporary plant communities. In some biomes, wildfire would be more frequent without human settlement; in others they would be unchanged or less frequent. A world without fire would have greater forest cover, especially in present-day savannas. Many species would be missing, because fire regimes have co-evolved with plant traits that resist, adapt to or promote wildfire. Certain plant traits are favoured by different fire frequencies, and may be missing in ecosystems that are normally fire-free. For example, post-fire resprouting is more common among woody plants in high-frequency fire regimes than where fire is infrequent. The impact of habitat fragmentation on wildfire crucially depends on whether the ecosystem is fire-adapted. In normally fire-free ecosystems, fragmentation facilitates wildfire starts and is detrimental to biodiversity. In fire-adapted ecosystems, fragmentation inhibits fires from spreading and fire suppression is detrimental to biodiversity. This interpretation explains observed, counterintuitive patterns of spatial correlation between wildfire and potential ignition sources. Lightning correlates positively with burnt area only in open ecosystems with frequent fire. Human population correlates positively with burnt area only in densely forested regions. Models for vegetation-fire interactions must be informed by insights from fire ecology to make credible future projections in a changing climate.We gratefully acknowledge support from the Leverhulme Centre for Wildfires, Environment and Society, who organized the virtual mini-workshop which initiated the writing of this paper. RKN is supported by the Leverhulme Centre. SPH and YS acknowledge support from the ERC-funded project GC2.0 (Global Change 2.0: Unlocking the past for a clearer future, Grant Number 694481). ICP, KJB and ND acknowledge support from the ERC-funded project REALM (Re-inventing Ecosystem And Land-surface Models, Grant Number 787203). JCH acknowledges funding from the ERC project SCATAPNUT (Grant Number 681885). This work is a contribution to the LEMONTREE (Land Ecosystem Models based On New Theory, obseRvations and ExperimEnts) project, funded through the generosity of Eric and Wendy Schmidt by recommendation of the Schmidt Futures program (SPH, YS and ICP)

Structural Properties of Central Galaxies in Groups and Clusters

Using a representative sample of 911 central galaxies (CENs) from the SDSS DR4 group catalogue, we study how the structure of the most massive members in groups and clusters depend on (1) galaxy stellar mass (Mstar), (2) dark matter halo mass of the host group (Mhalo), and (3) their halo-centric position. We establish and thoroughly test a GALFIT-based pipeline to fit 2D Sersic models to SDSS data. We find that the fitting results are most sensitive to the background sky level determination and strongly recommend using the SDSS global value. We find that uncertainties in the background translate into a strong covariance between the total magnitude, half-light size (r50), and Sersic index (n), especially for bright/massive galaxies. We find that n depends strongly on Mstar for CENs, but only weakly or not at all on Mhalo. Less (more) massive CENs tend to be disk (spheroid)-like over the full Mhalo range. Likewise, there is a clear r50-Mstar relation for CENs, with separate slopes for disks and spheroids. When comparing CENs with satellite galaxies (SATs), we find that low mass (<10e10.75 Msun/h^2) SATs have larger median n than CENs of similar Mstar. Low mass, late-type SATs have moderately smaller r50 than late-type CENs of the same Mstar. However, we find no size differences between spheroid-like CENs and SATs, and no structural differences between CENs and SATs matched in both mass and colour. The similarity of massive SATs and CENs shows that this distinction has no significant impact on the structure of spheroids. We conclude that Mstar is the most fundamental property determining the basic structure of a galaxy. The lack of a clear n-Mhalo relation rules out a distinct group mass for producing spheroids, and the responsible morphological transformation processes must occur at the centres of groups spanning a wide range of masses. (abridged)Comment: 22 pages, 14 figures, submitted to MNRA