Bottom-up Method Research Articles

Ultrafast Charge and Exciton Diffusion in Monolayer Films of 9-Armchair Graphene Nanoribbons.

Determining the electronic transport properties of graphene nanoribbons is crucial for assessing their suitability for applications. So far, this has been highly challenging both through experimental and theoretical approaches. This is particularly the case for graphene nanoribbons that are prepared by chemical vapor deposition, which is a scalable and industry-compatible bottom-up growth method that results in closely packed arrays of ribbons with relatively short lengths of a few tens of nanometers. In this study, the experimental technique of spatiotemporal microscopy is applied to study monolayer films of 9-armchair graphene nanoribbons prepared using this growth method, and combined with linear-scaling quantum transport calculations of arrays of thousands of nanoribbons. Both approaches directly resolve electronic spreading in space and time through diffusion and give an initial diffusivity approaching 200 cm2 s-1 during the first picosecond after photoexcitation. This corresponds to a mobility up to 550 cm2 V-1 s-1. The quasi-free carriers then form excitons, which spread with a diffusivity of tens of cm2 s-1. The results indicate that this relatively large charge carrier mobility is the result of electronic transport not being hindered by defects nor inter-ribbon hopping. This confirms their suitability for applications that require efficient electronictransport.

Recent developments on multiscale simulations for rheology and complex flow of polymers

AbstractThis review summarized the multiscale simulation (MSS) methods for polymeric liquids. Since polymeric liquids have multiscale characteristics of monomeric, mesoscopic, and macroscopic flow scales, MSSs that relate different hierarchical levels are adequate to reproduce flow properties accurately. Our review includes pioneering studies to the most advanced MSS studies on rheology predictions and flow simulations of polymeric liquids. We discuss two major types of MSS methods: the bottom-up and model-embedded MSS methods. The former method mainly connects all-atom molecular dynamics models and mesoscopic models to predict rheological properties. In contrast, the latter method, where a microscopic or mesoscopic model is embedded in a macroscopic computational domain, is designed to predict macroscopic flow properties. Finally, we also discuss MSS methods using machine learning techniques. Graphical abstract

Adaptive feature fusion and disturbance correction for accurate remaining useful life prediction of rolling bearings

As a key component in the transmission system of high-speed trains, bearings need to withstand certain loads while rotating at high speeds. Once a failure occurs, it can directly affect the safety of train operations. Therefore, it is of great significance to establish a reliable remaining useful life model to ensure train operation safety. Addressing the problems of redundant features in the existing multi-feature fusion process, which affects diagnostic performance, and the spurious fluctuations in the fusion features, which cause inaccurate determination of the start fault time and lead to low prediction accuracy of remaining useful life, we propose a method based on adaptive feature fusion and the autoregressive integrated moving average model for rolling bearing prediction. Firstly, features from the time domain, frequency domain, and entropy are extracted. A feature selection mechanism with minimum redundancy is constructed to screen the optimal sensitive feature set. Secondly, based on adaptive feature fusion, the optimal sensitive feature set is dynamically fused, and the spurious fluctuations of the health index are corrected using linear regression and the 3σ principle. Next, a bottom-up time series segmentation method is employed to divide the health status of the Improved Health Indicators. Finally, a remaining useful life prediction model based on the autoregressive integrated moving average model is established. This study demonstrates that the proposed method effectively identifies features that are most sensitive to degradation trends, accurately determines the initial fault moment of bearings, and achieves effective prediction of the remaining useful life of bearings.

Methods, Progress and Challenges in Global Monitoring of Carbon Emissions from Biomass Combustion

Global biomass burning represents a significant source of carbon emissions, exerting a substantial influence on the global carbon cycle and climate change. As global carbon emissions become increasingly concerning, accurately quantifying the carbon emissions from biomass burning has emerged as a pivotal and challenging area of scientific research. This paper presents a comprehensive review of the primary monitoring techniques for carbon emissions from biomass burning, encompassing both bottom-up and top-down approaches. It examines the current status and limitations of these techniques in practice. The bottom-up method primarily employs terrestrial ecosystem models, emission inventory methods, and fire radiation power (FRP) techniques, which rely on the integration of fire activity data and emission factors to estimate carbon emissions. The top-down method employs atmospheric observation data and atmospheric chemical transport models to invert carbon emission fluxes. Both methods continue to face significant challenges, such as limited satellite resolution affecting data accuracy, uncertainties in emission factors in regions lacking ground validation, and difficulties in model optimization due to the complexity of atmospheric processes. In light of these considerations, this paper explores the prospective evolution of carbon emission monitoring technology for biomass burning, with a particular emphasis on the significance of high-precision estimation methodologies, technological advancements in satellite remote sensing, and the optimization of global emission inventories. This study aims to provide a forward-looking perspective on the evolution of carbon emission monitoring from biomass burning, offering a valuable reference point for related scientific research and policy formulation.

Rethinking Nanoparticle Synthesis: A Sustainable Approach vs. Traditional Methods.

This review portrays a comparison between green protocols and conventional nanoparticle (NP) synthesis strategies, highlighting each method's advantages and limitations. Various top-down and bottom-up methods in NP synthesis are described in detail. The green chemistry principles are emphasized for designing safe processes for nanomaterial synthesis. Among the green biogenic sources plant extracts, vitamins, enzymes, polysaccharides, fungi (Molds and mushrooms), bacteria, yeast, algae, and lichens are discussed. Limitations in the reproducibility of green protocols in terms of availability of raw material, variation in synthetic protocol, and selection of material due to geographical differences are elaborated. Finally, a conclusion is drawn utilizing green chemical principles, & a circular economy strategy to minimize waste generation, offering a promising framework for the synthesis of NPs emphasizing sustainability.

Toolpath and Tool Design Innovations in Deformation Machining for Superior AA-7075 T6 Fins

Abstract A hybrid manufacturing process, deformation machining (DM) combines subtractive manufacturing with incremental forming. Monolithic components with complex profiles are created through the hybridization of manufacturing technologies, which also reduces the wastage of raw materials. The properties of geometry and surface quality of the fin made from the aluminum alloy Al-7075 T6 using the DM process’s bending mode was examined in this research, as were the effects of various approaches, toolpath methods, and tool design. To achieve the desired shape for the machined fin, various combinations of arcuate and slanting fin toolpath techniques were explored utilizing both top-down and bottom-up methods. The bottom-up method and the arcuate toolpath strategy were used to investigate various tool profiles. Using the best combination of toolpath strategy and tool profile, named as the T3 tool profile, which has a 0.6 mm nose radius and a circular sector, bottom-to-top (BTT)technique, and arcuate toolpath method, components with better geometrical features and surface roughness (SR) were produced. The analysis of the forming force in the bending approach of the DM progression was further carried out using this optimal combination.

A Comparative Study of Congo Red Textile Dye Photodegradation Using ZnO Al and ZnO Al+Mn Nanoparticles

Photodegradation of Congo Red textile dye was successfully carried out using ZnO Al and ZnO Al+Mn nanoparticles synthesized using the bottom-up coprecipitation method. Powder X-ray diffraction provides information that Al and Mn doping successfully inserted in the host crystal structure while maintaining the Hexagonal Wurtzite crystal structure. However, a shift in the diffraction peak caused by the lattice distance changes with the addition of doping. FE-SEM EDS provides information that both nanoparticles are nonuniform sphere-like due to the addition of dopping, and each dopping is detected through EDS spectra. There is a decrease in the gap energy in each sample. ZnO Al nanoparticles have a gap energy of 3.29 eV, and ZnO Al+Mn nanoparticles have a gap energy of 3.28 eV. However, the decrease in the gap energy is not directly related to the percentage and kinetic rate of Congo Red photodegradation. Adding 5 mg of ZnO Al Nanoparticles powder could degrade Congo Red up to 97.9 % within 120 min using UV A radiation or 0.03504/min. At the same time, adding ZnO Al+Mn Nanoparticles powder with the same parameters could only degrade 67.6 % or 0.00759/min. HIGHLIGHTS Energy Bandgap Modification of ZnO Nanoparticles The incorporation of Al single dopants and Al, Mn co-dopants into ZnO nanoparticles synthesized via a bottom-up coprecipitation method significantly alters the energy bandgap. Al single doping resulted in a bandgap of 3.29 eV, while Al, Mn double doping yielded a bandgap of 3.28 eV. Enhanced Photocatalytic Activity of Al-Doped ZnO Al-doped ZnO nanoparticles exhibited superior photocatalytic activity toward the degradation of Congo Red textile dye. With a photodegradation rate constant of 0.03504 min⁻¹, these nanoparticles achieved 97.9 % degradation of Congo Red within 120 minutes. Comparative Photocatalytic Performance of Al, Mn Co-Doped ZnO While Al, Mn co-doped ZnO nanoparticles were synthesized using the same method, their photocatalytic performance for Congo Red degradation was notably lower than that of Al-doped ZnO. The calculated photodegradation rate constant was 0.00759 min⁻¹, resulting in 67.6 % degradation within the same timeframe. GRAPHICAL ABSTRACT

Bottom-up Stripping of Graphene with Controlled Oxidation Behaviors towards Supercapacitors Energy Storage

Due to its distinctive two-dimensional planar structure, room temperature quantum Hall effect, and high strength, graphene has garnered significant interest in the fields of energy storage and conversion. In order to achieve high efficiency in the production of graphene, electrochemical peeling has been extensively investigated. Nevertheless, the intermolecular forces between graphite layers are disrupted during ion intercalation in solution, leading to inconsistent bonding forces and low yields. In order to address the issues above, this study introduces a novel bottom-up electrochemical peeling method, wherein graphite expansion occurs above the electrolyte. By preventing contact between the peeled graphene and the electrolyte, the oxidation of graphene is significantly minimized, resulting in a substantial yield of 88%. At the current density of 1.0 A g-1, the Go-QAS displayed 225.5 F g-1, and kept about 220.2 F g-1 after 500 cycles. The well-designed bottom-up peeling process leads to graphene nanosheets with reduced structural degradation, high purity, and excellent conductivity. This technique is expected to introduce innovative concepts for the field.

A top-down spatial scenario approach for identifying the locations of rainwater harvesting sites in an urban region.

Alternative water sources are necessary in developing nations because surface water is not always accessible, and groundwater is depleted. In such situations, rainwater harvesting is considered a promising sustainable water resource management solution. Numerous studies have been conducted to determine suitable locations for rainwater harvesting (RWH) using bottom-up approaches applied to large watersheds. The bottom-up methods begin with various geographic criteria and end with regions suitable for RWH intervention, even considering the distance from settlements to be one of the criteria, excluding urban areas from RWH site identification. This study developed a top-down methodology that began with the distributed pinpoint locations of potential RWH sites, as determined by distributed flow accumulation values produced from a digital elevation model (DEM), and then filtered out the sites based on various criteria in the context of urban areas. The flow accumulation values were apportioned according to the flow-contributing area of each RWH site. Five flow-contributing areal scenarios corresponding to 1 km2, 2.5 km2, 5 km2, 7.5 km2, and 10 km2 were considered in this study, as it is challenging to choose a suitable location for RWH sites in urban zones for efficient water storage owing to a variety of land uses. Based on this technique, a case study was conducted in Jaipur, Rajasthan, India, where it was found that the volumetric potential of rainwater storage is maximum (403,679,424.9 cu. m) for 1 km2 and minimum (169,951,322 cu. m) for 10 km2 flow contributing areal distribution per RWH site.

Particle-resolved direct numerical simulation and bottom-up statistical analysis on solid-phase pressure in particle–fluid systems

Solid-phase stress is a major factor shaping the complex behaviors of particle–fluid systems. However, uncertainties and inconsistencies can be found among the various models proposed. This work pays attention to the effect of mesoscale structures, that is, the heterogeneity of particle distribution at a scale of statistical meaning but no significant flow field variation, on the solid-phase normal stress, or pressure. Using a bottom-up statistical method, the solid-phase pressure in liquid- and gas–solid flows is investigated by particle-resolved direct numerical simulation (PR-DNS). It is found that, in almost uniform liquid–solid flows, the solid-phase pressure is not well predicted by classical kinetic theory of granular flow (KTGF) due to the presence of interphase force, although KTGF applies much better to single-phase granular flows. In gas–solid flows, with increasing heterogeneity described by structural functions, the statistical solid-phase pressure becomes significantly larger than that in corresponding homogeneous flows. It reveals that mesoscale structures strongly affect solid-phase pressure, and suggests the deviation from classical KTGF in heterogeneous gas–solid flows can be attributed to both the interphase forces (mainly drag) and mesoscale structures. The coefficients in the solid-phase pressure models in KTFG are modified, fitting the statistical results of both homogeneous and heterogeneous particle–fluid flows.

Effect of Al precursor’s properties on interactions with self-assembled monolayers for area selective deposition

Area selective deposition (ASD) is a high-precision atomic-level manufacturing technology that enables the development of bottom-up manufacturing methods in the future semiconductor field. The area selective deposition behavior of Al precursors on 1-octadecylthiol (ODT) passivated Cu/SiO2 surfaces was studied through experimental and theoretical analysis. The relationship between precursor steric hindrance, symmetry, penetration depth in ODT, and adsorption energy was elucidated. The loss of selectivity caused by different penetration depths of the precursor in ODT can be post-treated with acids or H2 plasma to remove the physisorption of precursor molecules between ODT chains, thereby improving the selectivity. Reliable ASD technology has been successfully applied to Cu/SiO2 patterns. Dimethylaluminum isopropoxide can selectively deposit about 10 nm of Al2O3 on SiO2 without detectable defects on the Cu area. This provides important insights into the choice of precursors in the ASD process and can extend its application to a wider range of device manufacturing schemes.

Assessing methane emissions and multiple benefits of mitigation through coupled crop-livestock system

A potent greenhouse gas, methane, contributes to global warming, primarily from livestock activities such as fermentation and manure management. Recognized as a low-carbon agricultural approach, coupled crop–livestock systems are gaining increasing attention for mitigating methane emissions and environmental impacts. This study focused on Inner Mongolia to estimate its methane reduction potential. We calculated methane emissions using a combination of bottom-up and top-down methods, analyzed livestock distribution, estimated manure production, and assessed the potential of manure to replace chemical fertilizers based on crop nutrient demand. Livestock emissions totaled approximately 769.0 kt. Adopting crop–livestock coupling could reduce emissions by 30.1 kt. Returning manure as fertilizer can cover 57.7% of cropland, saving 57% nitrogen and 20% phosphorus fertilizers, thus reducing costs by $296.3 million. The study has provided a scientific reference for developing methane emission reduction strategies, which can shed light on tackling climate change in the context of agricultural systems.

Barriers to the treatment of clubfoot in low- and middle-income countries: A guide to improve compliance

The Ponseti method of clubfoot treatment is ideal for low- and middle-income countries since it is minimally invasive and easily implementable through non-surgeon providers. However, variables related to the socioeconomic status of the community, patient-related factors, treatment provider-related factors, and health system-related factors may influence the outcome of this deformity. These barriers need a two-pronged approach, a top-down (involvement at the policy level) as well as a bottom-up method (involvement at an individual level). Improving the knowledge level of the caregiver as well as the provider and addressing the issues regarding financial constraints will help in eliminating various barriers to the treatment of clubfoot. Furthermore, regular audits help in identifying the efficacy of various solutions employed as well as identifying new barriers to treatment. Integration at the policy level with the involvement of governmental as well as non-governmental organisations helps to remove health system-level bottlenecks.

The Formation Mechanism of Carbonized Polymer Dots: Crosslinking-Induced Nucleation and Carbonization.

Carbon dots (CDs), as a kind of zero-dimensional nanomaterials, have been widely synthesized by bottom-up methods from various precursors. However, the formation mechanism is still unclear and controversial, which also brings difficulty to the regulation of structures and properties. Only some tentative formation processes were postulated by analyzing the products obtained at different reaction times and temperatures. Here, the effect of crosslinking on the formation of carbonized polymer dots (CPDs) is explored. Crosslinking-induced nucleation and carbonization (CINC) is proposed as the driving force for the formation of CPDs. Under hydrothermal synthesis, the precursors are initiated to polymerize and crosslink. The crosslinking brings higher hydrophobicity to generate the hydrophilic/hydrophobic microphase separation, which promotes dehydration and carbonization resulting in the formation of CPDs. Based on the principle of CINC, the influence factors of size are also revealed. Moreover, the dissipative particle dynamics (DPD) simulation is employed to support this formation mechanism. This concept of CINC will bring light to the formation process of CPDs, as well as facilitate the regulation of CPDs' size and photoluminescence.

Carbon metabolism modelling and regulation in Beijing

Land use and cover change leads to significant increases in urban carbon emissions. Considering the insufficiency of spatially fine-grained management in previous carbon emission studies, this study integrated top-down and bottom-up methods to simulate urban carbon metabolism. Metabolic actors were allocated to grids based on land use and POI data and then attributed to districts. Results indicated that from 2000 to 2018, Beijing's Carbon Imbalance Index increased by 55.81% in 2010 and declined by 12.46%. Carbon emission dominated the changes in the Carbon Imbalance Index, with the Production and Supply of Electric Power actors consistently accounting for over 25.96%, while the contribution of Urban and Transportation actors reached 20.11% and 27.74% in 2018, respectively. The metabolic actors exhibited a gradient distribution pattern from southeast to northwest. Artificial actors' carbon emissions decreased from the center to the periphery and spread along the road network, with the High-High cluster in the southeast mainly contributed by Manufacturing (22.33%) and the Production and Supply of Electric Power (20.16%) actors. Spatial distribution changes of Natural actors mainly occurred in Daxing and Chaoyang districts, whose absorption area extended by 45% and 37.26%, respectively, contributing to an increase in carbon absorption by 2.47 and 0.15 times. The Semi-natural actor exhibited net carbon emissions, with the High-High cluster in the southeast zone contributing 60.96% of emissions in 2000. We also categorized Beijing into four Areas based on the relationship between carbon emission and absorption. Targeted policy recommendations were formulated for different districts and metabolic actors.

Analyte-dependent Rabi splitting in solid-state plexcitonic sensors based on plasmonic nanoislands strongly coupled to J-aggregates

A key challenge in the field of plexcitonic quantum devices is the fabrication of solid-state, device-friendly plexcitonic nanostructures using inexpensive and scalable techniques. Lithography-free, bottom-up nanofabrication methods have remained relatively unexplored within the context of plexcitonic coupling. In this work, a plexcitonic system consisting of thermally dewetted plasmonic gold nanoislands (AuNI) coated with a thin film of J-aggregates was investigated. Control over nanoisland size and morphology allowed for a range of plasmon resonances with variable detuning from the exciton. The extinction spectra of the hybrid AuNI/J-aggregate films display clear splitting into upper and lower hybrid resonances, while the dispersion curve shows anti-crossing behavior with an estimated Rabi splitting of 180 eV at zero detuning. As a proof of concept for quantum sensing, the AuNI/J-aggregate hybrid was demonstrated to behave as a plexcitonic sensor for hydrochloric acid vapor analyte. This work highlights the possibility of using thermally dewetted nanoparticles as a platform for high-quality, tunable, cost-effective, and scalable plexcitonic nanostructures for sensing devices and beyond.

Comparison of compartmental analytical Blood-Oxygen-Level-Dependent functional Magnetic Resonance Imaging models against Monte Carlo simulations performed over cortical micro-angiograms.

Blood oxygen level-dependent functional magnetic resonance imaging (BOLD fMRI) arises from a physiological and physical cascade of events taking place at the level of the cortical microvasculature which constitutes a medium with complex geometry. Several analytical models of the BOLD contrast have been developed, but these have not been compared directly against detailed bottom-up modeling methods. Using a 3D modeling method based on experimentally measured images of mice microvasculature and Monte Carlo simulations, we quantified the accuracy of two analytical models to predict the amplitude of the BOLD response from 1.5 to 7T, for different echo time (TE) and for both gradient echo and spin echo acquisition protocols. We also showed that accounting for the tridimensional structure of the microvasculature results in more accurate prediction of the BOLD amplitude, even if the values for SO2 were averaged across individual vascular compartments. A secondary finding is that modeling the venous compartment as two individual compartments results in more accurate prediction of the BOLD amplitude compared with standard homogenous venous modeling, arising from the bimodal distribution of venous SO2 across the microvasculature in our data.

Heterogeneous evolution and driving forces of multiple hazardous air pollutants and GHGs emissions from China's primary aluminum industry

The booming of China's primary aluminum industry (PAI) brought substantial emissions of hazardous air pollutants (HAPs) and greenhouse gases (GHGs). By using life cycle assessment and bottom-up method, a comprehensive emission inventory for multiple typical HAPs and GHGs from China's PAI during 1990–2021 was developed and explored for the first time. Our results show that spatial-temporal emissions trends of HAPs and GHGs from PAI in China diverse significantly. The conventional atmospheric pollutants (including SO2, NOx and particulate matter (PM)), fluoride and per fluorinated compound (PFCs) had been effectively suppressed since 2007 due to the implementation of various environmental policies; while, emissions of CO, VOCs, CH4, heavy metals and CO2 had increased at different rates unexpectedly. From the spatial distribution perspective, Henan, Shanxi, Guizhou, Guangxi and Shandong dominated the emissions of PAI in China, but with consumption expansion and environmental constrains, PAI plants start to expand to northwest and southwest areas where are richer in sufficient and cheaper power resources, thus bring significant emission increasing there, particular for conventional atmospheric pollutants in northwest and CO and VOCs in southwest China. By underlying driving forces of PAI emissions, results show that end-of-pipe control measures at various stages have played different roles to reduce emissions of the concerned species at each period, but its reduction effect diminished gradually. Future reduction should seek underlying changes in production technology and energy system. Under constrains of environmental regulation and resource endowment, promoting circular economic development for PAI would be a key strategy to reduce HAPs and GHGs emissions simultaneously in PAI.

IMAGE CAPTION GENERATOR USING CNN AND LSTM

Machine learning is now all the rage in the AI world. We have recently used AI to construct very clever devices with exceptional performance. Deep learning is a subset of machine learning that produces very accurate findings, which in turn indicates very good performance. Apps for picture description make use of deep learning in our study. Providing a description of a picture's content is what image description is all about. Object and action detection in the input picture is the foundation of the notion. When describing images, there are primarily two methods: bottom-up and top-down. Bottom-up methods create captions by combining the information of an input picture. Using different architectures, such as recurrent neural networks, top-down methods provide a semantic representation of an input picture, which is then translated into a caption. One potential advantage of picture description is that it might aid those with visual impairments in comprehending what is shown in online images. What follows is an explanation of the specifics. Looking at the image below, what can you make out?

Enhancement of gaseous toluene removal by photocatalysis using hydrothermal synthesis carbon nanodots/TiO2 combined with zeolite ZSM-5

Abstract Many studies have been conducted to produce composite materials that possess the ability to transform volatile organic compounds (VOCs) to other nontoxic forms economically and environmentally by means of photocatalytic method. However, the main drawbacks of these materials include restricted surface area, low affinity towards organic molecules, and wide band gap energy which dramatically inhibit their performance. In this research, a composite material that surpasses the above disadvantages has been successfully synthesized from TiO2 - carbon nanodots (CDs) - zeolite ZSM-5. Particularly, CDs synthesized by bottom-up method were coated on TiO2 before being uniformly distributed on zeolite ZSM-5. The powder samples with varying CDs/TiO2 and zeolite ZSM-5 content were tested for their photocatalytic oxidization capability to determine the appropriate ratio. The results revealed that samples with higher zeolite content improved photocatalytic activity. Under other survey conditions such as low toluene flowrate, high relative humidity as well as high UV intensity, the photocatalytic performance was enhanced notably. The newly produced material has corrected most disadvantages of traditional photocatalysts, however, further researches need to be made to improve the removal stability.