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Facile and one-step preparation carbon quantum dots from biomass residue and their applications as efficient surfactants
摘要： Using biomass residue as a source of carbon precursors, a pyrolysis method was used to prepare biomass-derived luminescent Carbon Quantum Dots (CQDs). The prepared CQDs exhibited excellent fluorescence and luminescence properties and fluorescence behaviors of CQDs acquired at different pyrolysis temperatures varied. Importantly, the CQDs showed superior surface activity and the styrene-in-water Pickering emulsion prepared using the CQDs as nano-sized surfactant was highly stable: the higher the pyrolysis temperature the better the stability of the emulsion. In addition, there was no stratification found in the emulsion which was stabilized by the CQD500 (CQDs prepared at 500 (cid:1)C) after holding for 72 hours. This research provided an approach for preparing the surfactants of nano-sized particles in large scale. The CQDs prepared using the proposed methods are expected to have a high number of potential applications.
关键词： biomass,nano-sized surfactant,Carbon Quantum Dots,stability,pickering emulsion
更新于2025-11-14 17:04:02
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[IEEE 2018 7th International Conference on Renewable Energy Research and Applications (ICRERA) - Paris, France (2018.10.14-2018.10.17)] 2018 7th International Conference on Renewable Energy Research and Applications (ICRERA) - Biomass Free Piston Stirling Engine Generator with PV
摘要： Proposed hybrid electric power generation system is constituted from Free Piston Stirling engine generator and solar photovoltaic system. Free Piston Stirling engine electric generator uses biomass, such as wood pellets as the heat source. This system produces maximum output of 1.6kW and 45°C 200L hot water with linear generator. This paper studied the control of output voltage of Free Piston Stirling Engine. ILQ (Inverse Linear Quadratic) control theory is used to keep output active power between 400W and 500W. In long range time constant of burning system and in short range time constant of electrical circuit, experiment yields temperature of FPSEG head between upper limit and lower limit.
关键词： hybrid system,Free Piston Stirling Engine,biomass,solar power,ILQ control
更新于2025-09-23 15:23:52
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Methods for LiDAR-based estimation of extensive grassland biomass
摘要： Biomass estimation derived from Terrestrial Laser Scanning (TLS) is already an established technique in forestry, whereas TLS measurements are less well investigated for use in grassland ecosystems. Detailed information provided by survey systems can enhance management strategies and support timely measures. Field measurements were made in the “UNESCO biosphere reserve Rh?n” in Central Germany with a TLS station (Leica P30). Four methods for estimating biomass from 3d point clouds have been applied to the data, which were Canopy Surface Height (CSH), Sum of Voxel, Mean of 3d-grid Heights, and Convex-Hull. The optimum set of model specific parameters to increase model stability and performance was identified. The methods were compared in terms of model performance and calculation speed. For each method the effect of the number of scans used for each point cloud was assessed. The best fit for fresh biomass determination was achieved with a mean CSH value derived from the top 5% of all CSH values (adj. R2 0.72). In all cases, models for dry biomass estimation had less explanatory power than those for fresh biomass. CSH models based on point clouds, which were merged from two opposite scans, achieved the highest average accuracy both for fresh and dry biomass (adj. R2 0.73 and 0.58 respectively).
关键词： Biomass,TLS,Point cloud,Grassland,LiDAR
更新于2025-09-23 15:23:52
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Evaluation on Spaceborne Multispectral Images, Airborne Hyperspectral, and LiDAR Data for Extracting Spatial Distribution and Estimating Aboveground Biomass of Wetland Vegetation Suaeda salsa
摘要： Suaeda salsa (S. salsa) has a significant protective effect on salt marshes in coastal wetlands. In this study, the abilities of airborne multispectral images, spaceborne hyperspectral images, and LiDAR data in spatial distribution extraction and aboveground biomass (AB) estimation of S. salsa were explored for mapping the spatial distribution of S. salsa AB. Results showed that the increasing spectral and structural features were conducive to improving the classification accuracy of wetland vegetation and the AB estimation accuracy of S. salsa. The fusion of hyperspectral and LiDAR data provided the highest accuracies for wetlands classification and AB estimation of S. salsa in the study. Multispectral images alone provided relatively high user's and producer's accuracies of S. salsa classification (87.04% and 88.28%, respectively). Compared to multispectral images, hyperspectral data with more spectral features slightly improved the Kappa coefficient and overall accuracy. The AB estimation reached a relatively reliable accuracy based only on hyperspectral data (R2 of 0.812, root-mean-square error of 0.295, estimation error of 24.56%, residual predictive deviation of 2.033, and the sums of squares ratio of 1.049). The addition of LiDAR data produced a limited improvement in the process of extraction and AB estimation of S. salsa. The spatial distribution of mapped S. salsa AB was consistent with the field survey results. This study provided an important reference for the effective information extraction and AB estimation of wetland vegetation S. salsa.
关键词： multispectral image,Suaeda salsa,LiDAR data,fine classification,Aboveground biomass,hyperspectral image
更新于2025-09-23 15:23:52
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Estimating canopy structure and biomass in bamboo forests using airborne LiDAR data
摘要： The Bamboo species accounts for almost 1% of the Earth’s forested area with an exceptionally fast growth peaking up to 7.5–100 cm per day during the growing period, making it an unique species with respect to measuring and monitoring using conventional forest inventory tools. In addition their widespread coverage and quick growth make them a critical component of the terrestrial carbon cycle and for mitigating the impacts of climate change. In this study, the capability of using airborne Light Detection and Ranging (LiDAR) data for estimating canopy structure and biomass of Moso bamboo (Phyllostachys pubescens) was assessed, which is one of the most valuable and widely distributed bamboo species in the subtropical forests of south China. To do so, we first evaluated the accuracy of using LiDAR data to interpolate the underlying ground terrain under bamboo forests and developed uncertainty surfaces using both LiDAR-derived vegetation and topographic metrics and a Random Forest (RF) classifier. Second, we utilized Principal Component Analysis (PCA) to quantify the variation of the vertical distribution of LiDAR-derived effective Leaf Area Index (LAI) of bamboo stands, and fitted regression models between selected LiDAR metrics and the field-measured attributes such mean height, DBH and biomass components (i.e., culm, branch, foliage and aboveground biomass (AGB)) across a range of management strategies. Once models were developed, the results were spatially extrapolated and compared across the bamboo stands. Results indicated that the LiDAR interpolated DTMs were accurate even under the dense intensively managed bamboo stands (RMSE = 0.117–0.126 m) as well as under secondary stands (RMSE = 0.102 m) with rugged terrain and near-ground dense vegetation. The development of uncertainty maps of terrain was valuable when examining the magnitude and spatial distribution of potential errors in the DTMs. The middle height intervals (i.e., HI4 and HI5) within the bamboo cumulative effective LAI profiles explained more variances by PCA analysis in the bamboo stands. Moso bamboo AGB was well predicted by the LiDAR metrics (R2 = 0.59–0.87, rRMSE = 11.92–21.11%) with percentile heights (h25-h95) and the coefficient of variation of height (hcv) having the highest relative importances for estimating AGB and culm biomass. The hcv explained the most variance in branch and foliage biomass. According to the spatial extrapolation results, areas of relatively low biomass were found on secondary stands (AGB = 49.42 ± 14.16 Mg ha?1), whereas the intensively managed stands (AGB = 173.47 ± 34.16 Mg ha?1) have much higher AGB and biomass components, followed by the extensively managed bamboo stands (AGB = 67.61 ± 13.10 Mg ha?1). This study demonstrated the potential benefits of using airborne LiDAR to accurately derive high resolution DTMs, characterize vertical structure of canopy and estimate the magnitude and distribution of biomass within Moso bamboo forests, providing key data for regional ecological, environmental and global carbon cycle models.
关键词： Biomass,Bamboo,Leaf area index,LiDAR,Canopy structure
更新于2025-09-23 15:23:52
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[IEEE IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Valencia (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Wheel-Based Lidar Data for Plant Height and Canopy Cover Evaluation to Aid Biomass Prediction
摘要： Biomass estimation is fundamental for a variety of plant ecological studies. Direct measurement of aboveground biomass by clipping and sorting is destructive, time-consuming and laborious, thus reducing the ability of extensive sampling. Various plant traits, such as plant height, canopy cover, and leaf and plant structure contribute towards its biomass. In this study, we focus on exploiting wheel-based LiDAR data over an agricultural field to perform growth monitoring and canopy cover estimation, which would play a crucial role in the future to develop a non-invasive technique for biomass prediction.
关键词： Biomass,plant traits,LiDAR data,plant height,canopy cover
更新于2025-09-23 15:22:29
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Photocatalytic hydrogen evolution assisted by aqueous (waste)biomass under simulated solar light: Oxidized g-C3N4 vs. P25 titanium dioxide
摘要： Oxidized graphitic carbon nitride (o-g-C3N4) and Evonik AEROXIDE? P25 TiO2 were compared for lab-scale photocatalytic H2 evolution from aqueous sacrificial biomass-derivatives, under simulated solar light. Experiments in aqueous starch using Pt or Cu–Ni as the co-catalysts indicated that H2 production is affected by co-catalyst type and loading, with the greatest hydrogen evolution rates (HER) up to 453 and 806 μmol g?1 h?1 using TiO2 coupled with 3 wt% Cu–Ni or 0.5 wt% Pt, respectively. Despite the lower surface area, o-g-C3N4 gave HERs up to 168 and 593 μmol g?1 h?1 coupled with 3 wt% Cu–Ni or 3 wt% Pt. From mono- and di-saccharide solutions, H2 evolution was in the range 504–1170 μmol g?1 h?1 for Pt/TiO2 and 339–912 μmol g?1 h?1 for Cu–Ni/TiO2, respectively; o-g-C3N4 was efficient as well, providing HERs of 90–610 μmol g?1 h?1. The semiconductors were tested in sugar-rich wastewaters obtaining HERs up to 286 μmol g?1 h?1. Although HERs were lower compared to Pt/TiO2, a cheap, eco-friendly and non-nanometric catalyst such as o-g-C3N4, coupled to non-noble metals, provided a more sustainable H2 evolution.
关键词： Biomass,Graphitic carbon nitride,Hydrogen,Photocatalysis,Solar light,Titanium dioxide
更新于2025-09-23 15:22:29
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[IEEE IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Valencia (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Estimation of Forest Parameters Combining Multisensor High Resolution Remote Sensing Data
摘要： Forest monitoring is a major issue to carry out energetic and environmental policies. Actual context in spaceborne remote sensing data is very promising. Our study aims to test the ability of SAR, optical and textural data to estimate forest parameters (biomass, height, diameter and density), and to evaluate the improvement of combining these remote sensing data. We worked on monospecific pine forest stands. The first results highlighted the synergy between SAR and spatial texture informations. Sentinel-1 C-band SAR data is very promising for the estimation of forest parameters in monospecifics stands. Biomass was estimated with 29.4% relative error (20.7 tons/ha) and height with 14.6% (2.1m) combining four SAR and optical sensors.
关键词： Forest,biomass,texture,SAR,optical
更新于2025-09-23 15:22:29
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Proximal fluorescence sensing of potassium responsive crops to develop improved predictions of biomass, yield and grain quality of wheat and barley
摘要： Precision nutrient management requires accurate assessment of crop nutrient status. This is common for assessing N status, but much less so for other nutrients. Because fluorescence can indicate crop stress, the robustness of different fluorescence indices was assessed to predict crop nutrient status (K, Mg and Ca). The hypothesis was that crop nutrition limitations, especially K, can be detected using fluorescence proximal sensing to quantify crop response with a high degree of spatial resolution. A factorial experiment was imposed with four treatment factors: crop, K fertilizer rate, lime and row management. The soil at the experimental site was K deficient and the crop variables showed significant treatment effects (e.g. yield, protein). Fluorescence sensing identified a significant positive K response for three chlorophyll related indices (SFR_G, SFR_R and CHL), but not for FLAV; while wheat was significantly different from barley. Using a k-fold cross-validation method promising predictive relationships were found. The strongest predictions were for SFR_R to predict crop biomass, for SFR_G to predict crop K content of inter-row wheat, for CHL to predict crop Ca content of inter-row wheat and for FLAV with barley grain protein in the windrow treatment. The fluorescence indices produced more significant crop variable predictions than measuring NDVI using an active sensor. This study illustrates the utility of fluorescence sensing to measure chlorophyll related signals for capturing the nutritional status of barley and wheat crops. These results show encouraging potential to rapidly detect crop nutrient status for non-N nutrients using fluorescence sensing.
关键词： Biomass,Wheat,Fluorescence indices,Chlorophyll,Grain quality prediction,Barley
更新于2025-09-23 15:22:29
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Development of Near-Infrared Reflectance Spectroscopy (NIRS) Calibrations for Traits Related to Ethanol Conversion from Genetically Variable Napier Grass (Pennisetum purpureum Schum.)
摘要： Napier grass (Pennisetum purpureum Schum.) is one of the highest-yielding feedstocks for bio-based products and biofuel in semi-tropical areas of the USA and the world. Thirty genetically diverse Napier grass accessions were selected from a germplasm nursery in Tifton, GA and analyzed for fiber, ash, nitrogen (N) concentration, and biochemical conversion to ethanol. A near-infrared reflectance spectroscopy (NIRS) calibration was developed from this material to predict ethanol production, xylans, N concentration, and ash by separating leaves and stems and correlating with wet chemistry analyses. The high diversity of material from dwarf material with high leaf and stem digestibility to taller and more productive Napier grass cultivars resulted in high correlations with predicted results for in vitro dry matter digestibility (2 = 0.93), neutral detergent fiber (r2 = 0.83), acid detergent fiber (r2 = 0.95), ethanol (r2 = 0.90), nitrogen (r2 = 0.99), and ash (r2 = 0.98). This information will allow faster evaluation of Napier grass biomass for use by industry or geneticists.
关键词： Biomass,Near-infrared reflectance spectroscopy (NIRS),Forage,Biofuels
更新于2025-09-23 15:21:21