Flower Clusters Research Articles

Controlled design of Na–P1 zeolite/ porous carbon composites from coal gasification fine slag for high-performance adsorbent

Low-cost and concentrated industrial wastes have been recognized as a sustainable resource for preparation of new functional materials. Here, a new method was designed for the synthesis of porous composites containing high-purity Na–P1 zeolite and porous carbon from waste coal gasification fine slag (CGFS), which was treated first by acid leaching to controllably remove metal impurities and adjust Si/Al ratio, followed by NaOH fusion and hydrothermal treatment. By leaching with 1.0 mol/L HCl solution, the Si/Al ratio of the raw CGFS increased to 5.7, and the obtained CZ-1.0 consisted of high-purity Na–P1 zeolite with a typical cone-shaped flower cluster shape. The residue carbon in CGFS can be further activated to form porous carbon and graphite carbon layers interposed in the zeolite structure. The specific surface area and pore volume of CZ-1.0 reached 153.91 m2/g and 0.18 cm3/g, respectively. CZ-1.0 exhibited remarkable adsorption performance for methylene blue (MB) with the adsorption capacity reaching 137.5 mg/g for 100 mg/L MB solution. The adsorption process is mainly controlled by the chemisorption mechanism, and the adsorption of MB by CZ-1.0 may include ion exchange, hydrogen bond interaction, π-π bond interaction and van der Waals force. NaCl solution was successfully used as the desorption agent to regenerate the composite material, and the removal rate remained above 92% after five cycles. This work provides an effective strategy to synthesize a practically applicable adsorbent from the waste coal gasification fine slag for the purification of MB wastewater.

Evaluation of Cluster Bean (Cyamopsis tetragonoloba L.) Genotypes for Vegetable Pod Yield and Guar Gum Production

Cluster bean is a multipurpose legume crop grown in arid and semiarid regions for feed, fodder, and vegetable purpose. In the present study, seven genotypes of cluster bean were evaluated to estimate vegetable pod yield and guar gum properties under Coimbatore conditions. The experiment was laid out in Randomized Block Design with five replications. Observation on 14 characters viz., plant height (cm), days to first flowering, days to 50% flowering, number of flower cluster per plant, number of pods per cluster, number of pods per plant, pod length (cm), fresh pod weight (g), dry pod weight (g), fresh pod yield per plant (g), dry pod yield per plant (g), seed yield per plant (g), guar gum in seed (%) and protein content in seed (%) were recorded. The results of present investigation showed that CT CBE 002 outperformed all other genotypes studied in terms of plant height (156.34 cm), total number of clusters (16.44.), number of pods per cluster (6.78), number of pods per plant (99.64), seed yield per plant (29.61 g), guar gum percent (30.56 %), and seed protein content (22.13%). Pod weight was found to be the highest in MDU 1(6.62 g). CT CBE 001 was found to have the maximum pod yield per plant (343.12 g) and pod length (15.16 cm) and had the earliest days to first flowering (27.00 days) and days to 50% flowering (39.80 days).

Smart Agriculture: A Fruit Flower Cluster Detection Strategy in Apple Orchards Using Machine Vision and Learning

This paper presents the application of machine vision and learning techniques to detect and identify the number of flower clusters on apple trees leading to the ability to predict the potential yield of apples. A new field robot was designed and built to collect and build a dataset of 1500 images of apples trees. The trained model produced a cluster precision of 0.88 or 88% and a percentage error of 14% over 106 trees running the mobile vehicle on both sides of the trees. The detection model was predicting less than the actual amount but the fruit flower count is still significant in that it can give the researcher information on the estimated growth and production of each tree with respect to the actions applied to each fruit tree. A bias could be included to compensate for the average undercount. The resulting F1-Score of the object detection model was 80%, which is similar to other research methods ranging from an F1-Score of 77.3% to 84.1%. This paper helps lay the foundation for future application of machine vision and learning techniques within apple orchards or other fruit tree settings.

Increasing maleic anhydride selectivity for n-butane oxidation by Y-modified VPO catalysts

In the present study, a series of VPO catalysts with different amount of Y were prepared by organic phase method and activated in butane-air atmosphere. The catalytic performance was tested by the selective oxidation of n-butane to maleic anhydride (MA). The catalysts were characterized by SEM, TG, XRD, Raman, XPS, H2-TPR and NH3-TPD techniques. The characterization results showed that the addition of appropriate amount of Y can increase the amount and stability of the rose-like flower clusters of VPO precursors. Meanwhile, the addition of Y greatly increased the proportion of lattice oxygen on catalyst surface and changed the surface acidity of VPO catalyst. As a result, the selectivity and yield of MA were greatly improved by introducing the right amount of Y.

Crop Load Management in Nashi Pear—A Review

Most nashi cultivars require heavy thinning, and this has traditionally been performed by the time and labour-intensive practice of hand thinning. Crop load management is a key cost driver for nashi production, but there are limited cost-effective options available for nashi growers compared to other pome fruit, especially apples and, to a lesser extent, European pears. There is, however, potential to adapt some of the thinning tools and techniques used in apples and European pears to reduce the labour requirements and high cost of thinning in nashi, thus improving industry profitability. Several chemical thinning agents have potential for nashi, and an understanding of the optimal application rates, times and weather conditions for each chemical, as well as the conditions/factors that impact the tree carbon balance, will improve the predictability of chemical thinning. However, it is difficult to target specific flowers/fruitlets within a cluster with chemicals, and the flowers that produce the preferred fruit shape and size are in the middle of the flower cluster. Mechanical thinning during the flowering period with either Darwin or BAUM-style string thinners has potential, particularly as these devices can be used as early as flower emergence. As for chemical thinning, the issue of non-selectivity needs to be addressed; however, the development of mechatronic systems should overcome most problems that occur with the currently available mechanical thinners. Shading at critical times is an avenue that could be explored further to ascertain the critical stage when developing fruit are susceptible to enable the determination of the optimal timing and duration of shading. Targeted pruning and bud thinning during the dormant winter period to reduce the floral bud numbers is a valuable option for the precise placement of fruit in optimal positions and to set up the required number of clusters. This review highlighted several tools/techniques that, with further work, can be incorporated into a systematic approach to crop load management in nashi while reducing the risk and cost.

EVALUATION OF ADVANCED TOMATO MUTANTS BASED ON GROWTH, YIELD ATTRIBUTES AND YIELD

Field experiment was conducted during November 2009 to March 2010, to investigate growth and yield attributes of advanced tomato mutants (TM-110 and TM-219) along with three cultivars (BARI tomato-3, BARI tomato-14 and BINA tomato-5) following randomized complete block design with three replications. There were significant genotypic differences in respect of morpho-physiological characters (plant height, branch and leaf area, total dry mass, absolute growth rate and relative growth rate), biochemical parameter (nitrate reductase), reproductive characters (number of effective and non-effective flower cluster, number of flowers and reproductive efficiency), phenological characters (days to flowering start, days to first harvest and harvesting duration), yield attributes and fruit yield. In general high yielding genotypes showed superior performance in plant height, branch number, leaf area, total dry mass production, absolute growth rate, nitrate reductase activity and fruit size compared to low yielding ones. Relative growth rate,chlorophyll, photosynthesis, reproductive characters and fruit number had no relationship with fruit yield in tomato. The variety BARI tomato-14 showed the highest fruit yield (77.7 t ha-1) due to its superiority in respect of morpho-physiological, biochemical and larger fruit sizes. In contrast, the mutant TM-110 and the variety BINA tomato-5 produced the lower fruit yield (average 51.2 t ha -1) due to poor performance in growth characters and smaller size of fruits. However, TM-110 matured 4-15 days earlier than the other mutant/varieties. Further, harvesting duration was higher in high yielding genotypes than low yielding ones. The highest harvesting duration was recorded in BARI tomato-14 (28 days) followed by BARI tomato-3 (26 days). This information may be implicated in future plant breeding programme.

Unveiling the key factors affecting the grain size of NiCo2O4 during electrodeposition

NiCo2O4 bimetallic oxides are extraordinary ideal choice for supercapacitor electrode materials. As is widely known, the electrochemical properties of NiCo2O4 are strongly affected by its morphology. Then, how to obtain NiCo2O4 with ideal morphology has become a hot topic. In this paper, we found that the deposition rate ratio of Co2+ and Ni2+ is a decisive factor influencing the nucleation and growth of NiCo2O4 by the electrodeposition synthesis. When the deposition ratio of Co2+ and Ni2+ is close to 2.5:1, the nucleation rate of NiCo2O4 increases. NiCo2O4 nanoflakes with thinner and smaller morphology can be obtained. What’s more, we found that the deposition rate ratio of Co2+ and Ni2+ depends on the temperatures. Especially, the deposition rate ratio of Co2+ and Ni2+ can approach 2.5:1, when the electrodeposition temperature is 10 ℃. The obtained NiCo2O4 flower cluster nanosheets exhibited an outstanding capacitance of 1590 F·g−1 at the current of 1 A·g−1 and 1080 F·g−1 at the current of 20 A·g−1. More importantly, a large reversible capacitance of 1288 F·g−1 with a capacitance retention of 81.0 % can be obtained after 10,000 cycles at 1 A·g−1.

Degradation of cotton stalk lignin by carbon dots loaded copper oxide synergistic emulsion system

Based on the realization of efficient utilization of cotton stalk lignin, the degradation of cotton stalk lignin by a CDs/CuO synergistic emulsion system was investigated. Copper oxide (CuO) nanoparticles with monoclinic crystal structure were prepared and carbon dots (CDs) synthesized by microwave method was combined with CuO. Under visible light, water and n-butanol were used to construct a water–oil (W/O) emulsion reaction system to achieved depolymerisation of lignin into small molecule compounds. The involvement of hydrogen peroxide (H2O2) makes the degradation of lignin in this system even more effective. The final high value-added monophenolic compound of 57.70 mg g−1 was obtained, among which the most abundant were six monophenolic compounds such as vanillin, eugenol and vinyl guaiacol and so on. The results of GC-MS and FTIR characterization indicated that H-type monomers were the main products of lignin degradation in this system. The process conditions for lignin hydrogenolysis in this system were optimized and the best ratio of CDs/CuO was obtained by product analysis. There were characterized by SEM, TEM, XRD, XPS, FTIR, and US–vis. The results show that CDs/CuO aggregates into flower clusters, in which CDs are uniformly distributed on the surface of rhomboidal CuO monoliths. The analysis shows that the doping of CDs improves the absorption efficiency of CuO in the visible region, while reducing the complexation of CuO photogenerated electrons and holes, which achieves the purpose of improved photocatalytic activity of CuO.

Multi-class detection of kiwifruit flower and its distribution identification in orchard based on YOLOv5l and Euclidean distance

Asynchrony of kiwifruit flowering time results in different flower phenological stages in canopy at the same time. Pollination quality of flowers is influenced by their phenological stages, while their distributions determine fruit distributions and influence kiwifruit quality and yield. Thus, it’s necessary to find suitable flowers to be pollinated based on flower phenology and its distribution. However, influences of flower phenology and flower distribution were not considered in most previous studies about robotic pollination of kiwifruit, where pollination of all open flowers was indiscriminate. Therefore, a method was proposed for multi-class detection of kiwifruit flower and its distribution identification in orchard, which was based on You Only Look Once version 5 large (YOLOv5l) and Euclidean distance. According to kiwifruit flower phenology, kiwifruit flowers were classified into 10 classes to find suitable flowers for pollination, while flower cluster and branch junction were divided into 4 classes for obtaining flower distributions. All classes were manually labeled by rectangular bounding boxes for training and testing. Considering high detection accuracy requirements with small model size, YOLOv5l was applied to do transfer learning for multi-class detection of kiwifruit flower. Then, pixels coordinate of multi-class objects and their corresponding Euclidean distances could be gained. Finally, flower distributions in canopy were obtained by matching method. Total mean Average Precision (mAP) was 91.60 % in YOLOv5l, while the mAP of multi-class flower (10 classes) was 93.23 %, which was 5.70 % higher than that of the other 4 classes. Matching accuracy (MA) of flowers matching flower clusters was up to 97.60 %. Moreover, MA of flower cluster matching branch junction (96.20 %) and total MA (93.30 %) increased by 1.20 % and 1.00 % based on improved matching method, respectively. Total processing speed of multi-class flower detection and its distribution identification was 112.46 ms per image including 15.50 ms for image detection by YOLOv5l. Results showed that multi-class kiwifruit flowers and relative flower distributions could be fast and accurately obtained for further selecting suitable flowers for robotic pollination.

Exclusion and Repulsion of Popillia japonica (Coleoptera: Scarabaeidae) Using Selected Coverings on High Tunnel Structures for Primocane Red Raspberry.

Simple SummaryThe Japanese beetle, Popillia japonica Newman, has become an invasive pest of increasing concern for several high-value berry crops in the Midwest region of the U.S. since 2010. Due to the feeding injury caused by adult beetles, many producers have increased their use of foliar insecticides to manage P. japonica. With a goal to develop sustainable Integrated Pest Management (IPM) systems for raspberry production, we initiated a 3-year study to examine the potential for various high-tunnel production systems to suppress P. japonica populations, and thus minimize insecticide use in primocane (autumn maturing) raspberry. During each year of the study, we observed significant reductions in P. japonica beetle infestations (p < 0.05), under the high-tunnel, covered systems, compared to nearby open plots of raspberry.In temperate climates, there has been an increasing interest by fruit growers to implement the use of high tunnels, using a variety of coverings, to extend the season for fruit production. High tunnels also provide an opportunity to enhance insect pest management, via physical exclusion, and thus support reductions in insecticide use. Due to increasing pest pressure by the Japanese beetle, Popillia japonica Newman, in Midwest U.S. raspberry, a 3-year study (2017–2019) was conducted to evaluate the efficacy of selected high tunnel coverings to suppress adult beetle populations and minimize adult feeding injury. During each year of the study, P. japonica adult beetles were significantly reduced under poly-based coverings, with the ends open, and when a fine, nylon-mesh was used to cover the ends (p < 0.05). The poly-based covering also resulted in moderately higher ambient temperatures, which may have influenced beetle movement, including a “repellency effect” that encouraged beetles to exit the high tunnel structures. Although P. japonica adults are known to feed on raspberry flower clusters, including fruit, the majority (73–92%) of beetle feeding in this study was observed on the foliage. The impact of high tunnels on P. japonica are discussed within the context of developing sustainable Integrated Pest Management (IPM) programs for raspberry production.

A novel approach for estimating the flowering rate of litchi based on deep learning and UAV images.

Litchi flowering management is an important link in litchi orchard management. Statistical litchi flowering rate data can provide an important reference for regulating the number of litchi flowers and directly determining the quality and yield of litchi fruit. At present, the statistical work regarding litchi flowering rates requires considerable labour costs. Therefore, this study aims at the statistical litchi flowering rate task, and a combination of unmanned aerial vehicle (UAV) images and computer vision technology is proposed to count the numbers of litchi flower clusters and flushes in a complex natural environment to improve the efficiency of litchi flowering rate estimation. First, RGB images of litchi canopies at the flowering stage are collected by a UAV. After performing image preprocessing, a dataset is established, and two types of objects in the images, namely, flower clusters and flushes, are manually labelled. Second, by comparing the pretraining and testing results obtained when setting different training parameters for the YOLOv4 model, the optimal parameter combination is determined. The YOLOv4 model trained with the optimal combination of parameters tests best on the test set, at which time the mean average precision (mAP) is 87.87%. The detection time required for a single image is 0.043 s. Finally, aiming at the two kinds of targets (flower clusters and flushes) on 8 litchi trees in a real orchard, a model for estimating the numbers of flower clusters and flushes on a single litchi tree is constructed by matching the identified number of targets with the actual number of targets via equation fitting. Then, the data obtained from the manual counting process and the estimation model for the other five litchi trees in the real orchard are statistically analysed. The average error rate for the number of flower clusters is 4.20%, the average error rate for the number of flushes is 2.85%, and the average error for the flowering rate is 1.135%. The experimental results show that the proposed method is effective for estimating the litchi flowering rate and can provide guidance regarding the management of the flowering periods of litchi orchards.

Flower-like superhydrophilic and superaerophobic B-doped NiMoO4@NF as self-supported electrode for highly efficient hydrogen evolution

Designing cost-efficient and high activity electrocatalysts for hydrogen generation is significant for the large-scale and sustainable energy conversion. Herein, non-metallic boron (B) doping strategy was used to prepare self-supported electrode (B-NiMoO4@NF) which possessed flower cluster structure and was rich in oxygen deficient, and the influence of different addition of NaBH4 on catalyst performance was also investigated. Experimental studies showed that with the increase of NaBH4 addition, the morphology and structure of NiMoO4 precursor changed from "cuboid to hollow tubular to porous tubular". At the same times, numerous oxygen defect sites were generated that further increased the catalytic specific surface area and exposed more catalytic active sites. The results of surface wettability test showed that the prepared B-NiMoO4@NF electrodes exhibited superhydrophilic and superaerophobic properties, and B10-NiMoO4@NF electrode showed better HER catalytic performance than commercial Pt/C supported by nickel foam (Pt/C@NF). And it merely required an overpotential of 22.89 mV to achieve the current density of 10 mA cm−2 and the low Tafel slope of 30.08 mV dec−1 in 1.0 M KOH solution. Besides, the electrode also exhibited excellent HER stability at the high current density of the electrode (500 mA cm−2).

A Fine Line between Phytotoxicity and Blue When Producing Hydrangea macrophylla in a Nursery at a Low Substrate pH

Hydrangea macrophylla exhibiting blue sepals (versus purple or pink) have improved marketability; however, little research has been conducted to evaluate aluminum (Al), the element responsible for bluing, on crop growth, effectiveness of bluing sepals, and characteristics of flower clusters in an outdoor nursery. This study compared substrate Al availability, crop growth, flower color, number, and size over a 56-week period in two locations. A polymer coated (90-day release) or ground aluminum sulfate [Al2(SO4)3; water soluble] was either incorporated into a non-limed pine bark substrate, applied to the surface of the substrate as a top dress, or as a routinely applied Al2(SO4)3 drench (low concn.) or applied once (high concn.). In general, application of Al increased plant foliar Al concentration, but also decreased substrate pore-water pH and increased electrical conductivity (EC) with varying effects based on the applied product’s solubility and subsequent longevity. Aluminum sulfate increased the potential of Al phytotoxicity negatively affecting root morphology and creating an undesirable rhizosphere electrochemistry due to the pH being continually acidic, &lt;4, and the EC being temporarily increased to &gt;1.5 mS·cm−1. These suboptimal rhizosphere conditions resulted in a lower quality or smaller plant. No plants exhibited clear, deep blue flower cluster sought by consumers. Neither the effect of pore water pH or EC could, alone or in combination, account for the lack of plant vigor or blue flower clusters when substrate and foliar Al concentrations were adequate in flowering H. macrophylla. More research is needed to investigate the effect of pore-water electrochemical properties, possible mineral nutrient co-factors that provide Al synergisms or toxicity protections, and holistic plant health on ensuring blue coloration of a vigorous H. macrophylla.

Automatic flower cluster estimation in apple orchards using aerial and ground based point clouds

Chemical and mechanical thinning processes have long been used in stone and pome fruit production. During the thinning of apple flowers, growers use chemicals to regulate the tree load. Hand thinning is applied after the June drop to prune trees with excess crop load. The process of thinning can be unpredictable especially in biennial bearing cultivars. Thus, incentives to optimise chemical usage and to reduce expensive manual labour is ever increasing. Ground based machine vision systems have grown in popularity in orchard management due to the level of detail as well as plant coverage they can inspect with. Additionally, unmanned aerial vehicles (UAV) -based remote sensing technology is becoming a popular non-invasive quality inspection solution. This work proposes a framework for combining UAV and ground based RGB image data to detect flowering intensity in a Dutch Elstar apple orchard. The framework, based on point cloud reconstruction, presents automatic point cloud handling techniques as well as automated unsupervised flowering intensity estimation methods. Two linear regression models based on unsupervised machine learning methods were trained and validated from the framework that estimate flowering intensity in the orchard with both models having R 2 > 0.65, RRMSE < 20% and p-stat < 0.005 for the correlation between the image derived flower index and the flower cluster number counted in field. The proposed methods provide a novel strategy for guiding flower thinning using simple RGB images and location data only. Moreover, the proposed methods also reveal the flexibility of intra-tree inspection by checking its sub-volumes. • Automatic flower cluster estimation at the tree level with a R 2 = 0.7, RMSE < 20. • Ground and aerial point clouds have better performance when combined. • Novel strategy developed for guiding flower thinning in orchard management. • Dataset with aerial and ground images, and field sampling data released.

Effect of foliar spray of gibberellic acid (GA3) and borax on growth, productivity, and quality of tomato (Lycopersicon esculentum Mill.) under protected structure at Kaski, Nepal

Tomato (Lycopersicon esculentum Mill.) is a highly demanded solanaceous vegetable crop in Nepal. Poor vegetative growth, low flowering and fruit set are the major problems in tomato. Thus, an experiment was conducted to improve vegetative growth, flowering, fruiting and productivity of tomato (Nepali Variety Type 2) at Pokhara-14, Kaski, Nepal during spring- summer of 2019 in Split plot arrangement with gibberellic acid (GA3) as main factor and Borax as sub factor, with three replications. The main factor consisted of four concentrations of GA3 (0, 25, 50 and 75 ppm) which was applied thrice as foliar spray at 15, 30 and 45 days after transplanting (DAT) and sub factor consisted three concentrations of borax (0%, 0.25% and 0.5%) which was applied four times as foliar spray at 15, 30, 45 and 60 DAT. Growth characters viz., plant height, leaf area, leaf number, flower number per cluster, fruit number were significantly enhanced with application of 50 ppm GA3 and gave higher productivity (31.65 t ha-1) compared to other concentrations and control. Likewise, foliar spray of borax resulted in higher stem girth, flower clusters, number of flowers per cluster, and TSS content of tomato. Furthermore, borax @ 0.25% gave the highest productivity (30.78 t ha-1). The better effect of 50 ppm GA3 on growth and yield of tomato and effect of 0.25% borax on flowering and yield, makes them promising solutions to the problems of tomato at Kaski, Nepal. Thus, further research should be conducted by using GA3 and borax in several other tomato cultivars in field and under protected structure at different soil type and climatic conditions.

Pharmacognostic and Pharmacological Properties of Eriodictyon crassifolium benth: A Review

Yerba Santa (Eriodictyon crassifolium) is a shrub which have large and wooly leaves. It has an attractive cluster of flowers. Native Americans use leaves of this plant to make tea or syrups. It is found in Southern California up to 4000 feet. It is also known as Felt leaved yerba santa which is a dicot angiosperm belonging to the family Boraginaceae. It is used to treat cold, cough and fever. Several species of Eriodictyon showed anti-inflammatory and neuroprotective activities. The presence of flavanones sterubin, homoeriodictyol and eriodictyol measured by mass spectrometry method. It is also used in the treatment of Alzheimer’s disease. It has shown its potential for antioxidant and anticancer benefits. The Kawaiisu tribe used tea of yerba santa to treat gonorrhea. The tribe community known as ‘chumash’ used its leaves for the purpose of treating diseases like asthma, pneumonia. The whole plant is used in the medicinal purpose including leaves, flowers and stems. Chief chemical constituent is flavanones. Other than flavanones, it also contains flavonoids, tannins and volatile oils.

Rachis detection and three-dimensional localization of cut off point for vision-based banana robot

For the operation and visual positioning of a banana robot, it is important to accurately position the rachis and cut off point. However, the main factors that affect the three-dimensional (3D) positioning accuracy of the point of cutting off male flower clusters are the image detection algorithm and positioning errors in the depth direction. In this study, a new YOLOv5-B model was constructed using the InvolutionBottleneck module in the network structures of YOLOv5 and improving the loss function to improve the accuracy and speed of small target detection. Then, the contour of the rachis is segmented using an edge detection algorithm, and the optimal cut off point is obtained as a scalar. Finally, a robot experiment platform with stereo vision for cutting off male flower clusters of bananas is built to obtain the 3D space coordinates of the cutoff point of the rachis through visual inspection and analyze positioning errors in the depth direction. The experiments show that the overall mean average position (mAP) of the YOLOv5-B model for multi-target recognition of bananas is 93.2%, which is higher than that of the YOLOv5 and PP-YOLOv2 models. Among these, the small-target detection accuracy of the proposed model is better than that of the others. Additionally, the YOLOv5-B model shows excellent speed, with an average image processing time of only 0.009 s/piece. An accurate relation model for the geometric measurement between the depth camera and laser measurement was established, and positioning errors were analyzed. The median error (MEDE) of the depth coordinates was 8 mm and the median absolute deviation (MAD) was 2 mm. If the depth error is within the control range of the compensation, the requirements of rachis segmentation by robots can be met.

Source:sink relationship in the apple flower cluster

Source:sink relationship in the apple flower cluster

Construct 3D NiCo-LDH/Cu2O p-n heterojunction via electrostatic self-assembly for enhanced photocatalytic hydrogen evolution

Hydrogen evolution of inorganic photocatalysts is currently a hot research topic. In order to prepare highly efficient catalysts for hydrogen evolution, p-n type heterojunctions were successfully constructed by closely combining p-type Cu2O and n-type NiCo-LDH with opposite electrical properties by a simple electrostatic self-assembly. In addition, because the positions of the two single catalysts NiCo-LDH and Cu2O Fermi levels are dissimilar from those of traditional p-n heterojunctions, the extremely high movement of Fermi levels forms a strong built-in electric field, which powerfully prevents the gradient diffusion of charge and facilitates the migration of intrinsic photocarriers. Furthermore, the flower cluster NiCo-LDH is composed of two-dimensional thin sheet structure, which has a large specific surface area and can prevent Cu2O agglomeration. Under the condition of using triethanolamine solution and eosin Y (EY) respectively as sacrifice reagent reducing agent and photosensitizer, the composite catalyst has reached 18332 μmol·g−1 in 5 h, which was increased by 8.5 times compared to the pure Cu2O. This research provides an innovative strategy to construct a heterojunction between a metal double-layer hydroxide and a non-noble metal oxide to realize an effective photocatalytic hydrogen production.

Assembling Hollow Cactus-Like ZnO Nanorods with Dipole-Modified Graphene Nanosheets for Practical Room-Temperature Formaldehyde Sensing

Formaldehyde (HCHO) sensing plays a critical role for indoor environment monitoring in smart home systems. Inspired by the unique hierarchical structure of cactus, we have prepared a ZnO/ANS-rGO composite for room-temperature (RT) HCHO sensing, through assembling hollow cactus-like ZnO nanorods with 5-aminonaphthalene-1-sulfonic acid (ANS)-modified graphene nanosheets in a facile and template-free manner. Interestingly, it was found that the ZnO morphology could be simply tuned from flower clusters to hollow cactus-like nanostructures, along with the increase of the reaction time during the assembly process. The ZnO/ANS-rGO-based sensors exhibited superior RT HCHO-sensing performance with an ultrahigh response (68%, 5 ppm), good repeatability, long-term stability, and an outstanding practical limit of detection (LOD: 0.25 ppm) toward HCHO, which is the lowest practical LOD reported so far. Furthermore, for the first time, a 30 m3 simulation test cabinet was adapted to evaluate the practical gas-sensing performance in an indoor environment. As a result, an instantaneous response of 5% to 0.4 ppm HCHO was successfully achieved in the simulation test. The corresponding sensing mechanism was interpreted from two aspects including high charge transport capability of ANS-rGO and the distinct gas adsorbability derived from nanostructures, respectively. The combination of a biomimetic hierarchical structure and supramolecular assembly provides a promising strategy to design HCHO-sensing materials with high practicability.