Bottom-up Method Research Articles

(Mg,Mn,Fe,Co,Ni)O: A rocksalt high-entropy oxide containing divalent Mn and Fe.

High-entropy oxides (HEOs) have aroused growing interest due to fundamental questions relating to their structure formation, phase stability, and the interplay between configurational disorder and physical and chemical properties. Introducing Fe(II) and Mn(II) into a rocksalt HEO is considered challenging, as theoretical analysis suggests that they are unstable in this structure under ambient conditions. Here, we develop a bottom-up method for synthesizing Mn- and Fe-containing rocksalt HEO (FeO-HEO). We present a comprehensive investigation of its crystal structure and the random cation-site occupancy. We show the improved structural robustness of this FeO-HEO and verify the viability of an oxygen sublattice as a buffer layer. Compositional analysis reveals the valence and spin state of the iron species. We further report the antiferromagnetic order of this FeO-HEO below the transition temperature ~218 K and predict the conditions of phase stability of Mn- and Fe-containing HEOs. Our results provide fresh insights into the design and property tailoring of emerging classes of HEOs.

Informing Methane Emissions Inventories Using Facility Aerial Measurements at Midstream Natural Gas Facilities.

Increased interest in greenhouse gas (GHG) emissions, including recent legislative action and voluntary programs, has increased attention on quantifying and ultimately reducing methane emissions from the natural gas supply chain. While inventories used for public or corporate GHG policies have traditionally utilized bottom-up (BU) methods to estimate emissions, the validity of such inventories has been questioned. Therefore, there is attention on utilizing full-facility measurements using airborne, satellite, or drone (top-down (TD)) techniques to inform, improve, or validate inventories. This study utilized full-facility estimates from two independent TD methods at 15 midstream natural gas facilities in the U.S.A., which were compared with a contemporaneous daily inventory assembled by the facility operator, employing comprehensive inventory methods. Estimates from the two TD methods statistically agreed in 2 of 28 paired measurements. Operator inventories, which included extensions to capture sources beyond regular inventory requirements and integration of local measurements, estimated significantly lower emissions than the TD estimates for 40 of 43 paired comparisons. Significant disagreement was observed at most facilities, both between the two TD methods and between the TD estimates and operator inventory. These findings have two implications. First, improving inventory estimates will require additional on-site or ground-based diagnostic screening and measurement of all sources. Second, the TD full-facility measurement methods need to undergo further testing, characterization, and potential improvement specifically tailored for complex midstream facilities.

A robust nanoscale RP HPLC-MS approach for sensitive Fc proteoform profiling of IgG allotypes

The conserved region (Fc) of IgG antibodies dictates the interactions with designated receptors thus defining the immunological effector functions of IgG. Amino acid sequence variations in the Fc, recognized as subclasses and allotypes, as well as post-translational modifications (PTMs) modulate these interactions. Yet, the high similarity of Fc sequences hinders allotype-specific PTM analysis by state-of-the-art bottom-up methods and current subunit approaches lack sensitivity and face co-elution of near-isobaric allotypes.To circumvent these shortcomings, we present a nanoscale reversed-phase (RP) HPLC-MS workflow of intact Fc subunits for comprehensive characterization of Fc proteoforms in an allotype- and subclass-specific manner. Polyclonal IgGs were purified from individuals followed by enzymatic digestion releasing single chain Fc subunits (Fc/2) that were directly subjected to analysis. Chromatographic conditions were optimized to separate Fc/2 subunits of near-isobaric allotypes and subclasses allowing allotype and proteoform identification and quantification across all four IgG subclasses. The workflow was complemented by a semi-automated data analysis pipeline based on the open-source software Skyline followed by post-processing in R. The approach revealed pronounced differences in Fc glycosylation between donors, besides inter-subclass and inter-allotype variability within donors. Notably, partial occupancy of the N-glycosylation site in the CH3 domain of IgG3 was observed that is generally neglected by established approaches. The described method was benchmarked across several hundred runs and showed good precision and robustness.This methodology represents a first mature Fc subunit profiling approach allowing truly subclass- and allotype-specific Fc proteoform characterization beyond established approaches. The comprehensive information obtained paired with the high sensitivity provided by the miniaturization of the approach guarantees applicability to a broad range of research questions including clinically relevant (auto)antibody characterization or pharmacokinetics assessment of therapeutic IgGs.

Comparison of a Bottom-Up GNSS Radio Occultation Method to Measure D- and E-Region Electron Densities with Ionosondes and FIRI

High-frequency skywave propagation can be heavily impacted by D- and E-region dynamics requiring accurate global measurements to optimize performance. A standard measurement technique is to use ionosondes, but they are unable to measure below 1 MHz and are only available at a limited number of land-locked sites around the globe. In contrast, the Global Navigation Satellite System radio occultation (GNSS-RO) bottom-up method is a new approach specifically designed to generate electron density profiles in the D- and E- region ionosphere. It takes advantage of satellite constellations that currently provide over 20,000 daily measurements and global coverage. In this paper, GNSS-RO profiles were compared against ionosonde profiles at four sites covering a wide latitudinal range, and FIRI modeled profiles corresponding to the same latitude and local solar time. This comparison was completed using daytime profiles when sporadic-E (Es) was not present. The average GNSS-RO profile is found to be a few kilometers higher in altitude than the ionosonde profiles at the minimum frequency, fmin. When the ionosonde profiles are shifted so that the altitudes match at fmin, they are in good agreement up to the E-region peak altitude, hmE. Below fmin, the GNSS-RO profile is in good agreement with the FIRI profile, indicating that the profiles can measure the D- to E- transition region. The frequency of the E-region peak, foE, showed general agreement between the GNSS-RO and ionosonde measurements; however, the hmE agreement was weaker and the GNSS-RO profiles tend to have an hmE in a narrow altitude range for all profiles. Virtual heights were simulated for the GNSS-RO profiles using a numerical ray tracer for direct comparison with ionosonde observations, which showed agreement for many of the virtual heights near fmin, but also indicated a positive bias in the GNSS-RO virtual heights that may be due to low foE or elevated hmE estimates. For a quiet ionosphere, the shifted GNSS-RO electron density profiles were a good match for both measured ionosonde profiles and modeled FIRI profiles and the method is capable of providing global coverage of the D- and E-regions. Future work will require more data for seasonal and morning–afternoon comparisons as well as comparisons for the disturbed ionosphere when the sporadic-E layer is present.

Unexpected moderate contribution of intermediate volatility organic compounds from gasoline vehicle emission to secondary organic aerosol formation in summer of Beijing

Intermediate volatility organic compounds (IVOCs) have been proposed to be important contributors to secondary organic aerosol (SOA) formation. In this study, we characterized ambient IVOCs in Beijing and performed a comprehensive closure study of ambient SOA production using top-down (photochemical-age-based parameterization method to apportion OA) and bottom-up (estimation of SOA from detailed speciation of IVOCs and VOCs) approaches. The IVOCs in Beijing were more oxidized and had a high abundance (62.5 ± 45.2 μg m−3), significantly higher than in the US. Unlike the US, the IVOCs in Beijing shows a hybrid characteristic of gasoline and vehicle exhausts and are influenced by complicated sources. A ∼ 80% SOA mass closure could be reached between the top-down and bottom-up methods, in which IVOCs was the dominant contributor (75.5%). Furthermore, we established a parametric method (SOA/CO vs. photochemical age) to estimate the contribution of vehicular SOA to ambient OA. Unexpectedly, the contributions of gasoline vehicles to ambient OA are only 7.3% for POA and 13.0% for SOA. Our results imply that IVOCs from other sources, e.g., diesel vehicles and cooking emissions, may be important precursors to secondary organic aerosol in Beijing. In addition, this study highlights the importance of comprehensively performing the molecular identification of IVOCs from different emissions in China. Plain language summarySecondary organic aerosol (SOA), formed by multigenerational oxidation of gas-phase compounds, represents a significant fraction of organic aerosol. However, current models often underestimate the measured SOA mass. One of the ubiquitous reasons is the missing measurement of the intermediate volatility organic compounds (IVOCs). Here we quantified and qualified the IVOCs in Beijing and performed a comprehensive SOA study using bottom-up and top-down methods. Besides, we estimate the SOA formation by gasoline vehicles. Unexpectedly, the contributions of gasoline vehicles to ambient OA are only 7.3% for POA and 13.0% for SOA, highlighting the importance of studying IVOCs from other sources, e.g., diesel exhausts and cooking emissions.

Graphene: A State-of-the-Art Review of Types, Properties and Applications in Different Sectors

Being one of the most lightweight, toughest, and most flexible materials in the world, having exceptional mechanical, electrical, thermal, and tribological properties, makes graphene and its several based materials crucial applicants for biomedical, medical, aerospace, automotive, electrical, energy, sensing, food, and other industrial sectors. Graphene is classified as a carbon allotrope, with one-thick-atom planar 〖sp〗^2 layers of attached carbon atoms tightly loaded within a crystal honeycomb lattice. Various techniques to produce graphene were established over a brief period of time, including top-down and bottom-up methods. Graphene itself can be used as a coating to improve other materials; likewise, it could be added to further composite materials. This review recaps the applications of graphene, its types, synthesis methods, and mechanical, tribological, and thermal characteristics, in addition to enhancement methods for graphene and its based materials.

Multilevel Feature Fusion for End-to-End Blind Image Quality Assessment

In this paper, a framework based on two feature extraction networks and a multilevel feature fusion (MFF) network is proposed. Multilevel degradation features can be obtained through this method, and combined with the human visual perception system, the local and global feature information contained in these features can be captured, which is conducive to the prediction of distorted images. First, a restored image approximating a reference image is generated by a restorative generative adversarial network (GAN). Furthermore, the multilevel degradation features of distorted images and the restored image features are extracted by EfficientNet. Second, the features extracted by EfficientNet are input into the MFF network and are fully expressed by the top-down, bottom-up and third edge joining methods. Moreover, the features provide more low-level details and high-level semantic features for the prediction of image quality scores. In addition, after the MFF stage, the framework calculates the score of each branch feature and obtains the average quality score. Experimental results show that our method achieves greatly improved prediction accuracy and performance on five standard databases.

Ab Initio Study of Charge Separation Dynamics and Pump-Probe Spectroscopy in the P3HT/PCBM Blend.

We develop a bottom-up computational method for excited-state dynamics and time-resolved spectroscopy signals in molecular aggregates, on the basis of ab initio excited-state calculations. As an application, we consider the charge separation dynamics and pump-probe spectroscopy in the amorphous P3HT/PCBM blend. To simulate quantum dynamics and time-resolved spectroscopy, the model Hamiltonian for single-excitation and double-excitation manifolds was derived on the basis of fragment-based excited-state calculations within the GW approximation and the Bethe-Salpeter equation. After elucidating the energetics of the electron-hole separation and examining linear absorption spectrum, we investigated the quantum dynamics of exciton and charge carriers in comparison with the pump-probe transient absorption spectra. In particular, we introduced the pump-probe excited-state absorption (ESA) anisotropy as a spectroscopic signature of charge carrier dynamics after exciton dissociation. We found that the charge separation dynamics can be probed by the pump-probe ESA anisotropy dynamics after charge-transfer excitations. The present study provides the fundamental information for understanding the experimental spectroscopy signals, by elucidating the relationship between the excited states, the exciton and charge carrier dynamics, and time-resolved spectroscopy.

Hierarchical SAPO-34 Catalysts as Host for Cu Active Sites.

Cu-containing hierarchical SAPO-34 catalysts were synthesized by the bottom-up method using different mesoporogen templates: CTAB encapsulated within ordered mesoporous silica nanoparticles (MSNs) and sucrose. A high fraction of the Cu centers exchanged in the hierarchical SAPO-34 architecture with high mesopore surface area and volume was achieved when CTAB was embedded within ordered mesoporous silica nanoparticles. Physicochemical characterization was performed by using structural and spectroscopic techniques to elucidate the properties of hierarchical SAPO-34 before and after Cu introduction. The speciation of the Cu sites, investigated by DR UV-Vis, and the results of the catalytic tests indicated that the synergy between the textural properties of the hierarchical SAPO-34 framework, the high Cu loading, and the coordination and localization of the Cu sites in the hierarchical architecture is the key point to obtaining good preliminary results in the NO selective catalytic reduction with hydrocarbons (HC-SCR).

NOx Emissions Control Area (NECA) scenario for ports in the North Adriatic Sea

In response to global warming, the International Maritime Organisation (IMO) set rules of 50% Greenhouse Gas (GHG) reduction by 2050, from 2008 levels. Signatory countries to the IMO's regulation require frequent assessment of the contribution of GHG emissions from shipping calling at their ports or trading in their territorial waters to ensure their compliance with the regulations. This demands a rapid and accurate method to assess shipping's contribution to GHG emissions.Current methodologies for estimating emissions from ships can be described on a scale between bottom-up and top-down methods. Top-down methods provide rapid estimates – primarily based on fuel sales reports - without considering individual vessel details. Therefore, they are less accurate and do not provide a breakdown of emissions by ship types or in specific regions. Bottom-up methodologies are detailed vessel-based estimates; however, they are data and time-demanding.The Ship Emissions Assessment method (SEA) (Topic et al., 2021) fills the gap between bottom-up and top-down methods by providing an innovative hybrid solution for rapid but accurate ship emission estimation. It uses publicly available, cost-effective data sets for emission estimates. The SEA method is capable of estimating ships' emissions in designated areas to understand regulations' effectiveness and provide emission quantification evidence. This research objective was to apply the SEA method to quantify CO2, SOX and NOX exhaust emissions from containerships for the three crucial containership ports: Trieste, Rijeka and Venice, in the North of the Adriatic Sea.The SEA methodology was applied to assess emissions and forecast efficiency in scenarios of different regulatory measures. A reduction in NOx emissions was estimated for the event of the implementation of NECA in all three ports. Results showed that 447.13 tonnes of NOx could be reduced each year in the North Adriatic Sea area around the ports of Rijeka, Trieste and Venice in the event that NECA regulations are stipulated.

Energy consumption and emissions analysis of large container seaports considering the impact of COVID-19: A case study of Ningbo Zhoushan Port

Ports play critical roles in modern society as they connect water and land transportation and integrate transportation with energy systems, leading to high demand of various types of energy and emissions from various sources. Based on high quality of data from the ports authority, energy consumption characteristics of cargo handling equipment and ships in Chuanshan Port Area of Ningbo Zhoushan Port are analyzed. Besides, emissions of cargo handling equipment and ships are evaluated using fuel-based top-down and activity-based bottom-up methods, respectively. Moreover, as a reflection on the impact of COVID-19 on ports, energy consumption patterns indicate the resilience of port operation. The results reveal different energy requirement of various types of energy resources and show that container trucks, rubber-tire gantry and berthed ships are main sources of several kinds of emissions in the considered port. The findings of this study are significant in providing support for port authorities to adopt measures towards achieving a green and sustainable port.

The multi-scenario projection of cost reduction in hydrogen production by proton exchange membrane (PEM) water electrolysis in the near future (2020–2060) of China

The production and utilization of green hydrogen is of utmost importance to achieve the dual carbon national strategy of China, i.e., carbon peaking in 2030 and carbon neutrality in 2060. The proton exchange membrane water electrolysis (PEMWE), with the advantage of the fast dynamic operation adapting to the non-uniformity and intermittency of renewable energy, is capable of producing hydrogen with higher purity. To calculate hydrogen production costs from 2020 to 2040, we used the bottom-up method to determine the capital cost of PEM electrolyzers, which is brought into the H2A model along with electricity price predicted for China. In 2040–2060, the learning curve was used to predict the decline of the capital cost of the electrolyzer. We analyzed two scenarios during this period, considering whether the operational performance of the electrolyzer such as voltage and current density is improved. The calculated cost of hydrogen production will reduce from 7.26 $/kg to 2.60 $/kg mainly because of the capital cost reduction of the electrolyzer system from 4450 $/kW to 812 $/kW and the decline of the electricity cost. The capital cost and electricity cost account for 47.6% and 34.7% of the current total cost, respectively, while the electricity cost is projected to be the dominant and will account for 56.4% of the total cost in the scenario of 2040–2060. The cost price of 2.60 $/kg will show an advantage when the carbon tax of 78.82 $/t CO2 eq. for coal gasification and 107.48 $/t CO2 eq. for steam-methane reforming are imposed.

Developing materials and energy-related indicators and identifying barriers to their adoption in an adjustable urban road sustainable rating system

Materials and energy consumption contribute significantly to the carbon footprint of roadway construction and operation. Although material and energy-related indicators (MEIs) have been incorporated into existing sustainability rating systems in developed countries, their application to other countries/regions, especially developing countries, might be hindered because of varying local contexts. Given that no rating system is specified for urban roadway projects, this study aims to develop MEIs and their weighting system as a part of an adjustable urban road sustainable rating system (AUR-SRS) and investigate the important barriers to MEI adoption. To this end, the adaptive approach integrating top-down and bottom-up methods was employed. The top-down approach used the comprehensive literature review to derive potential MEIs and barriers. The bottom-up approach referred to expert opinions in a three-country context (i.e., Taiwan, Indonesia, and Vietnam) to finalize the five most suitable MEIs and 26 corresponding requirements. The study then applied the Analytical Hierarchy Process (AHP) and Weighted Sum Model (WSM) methods to assign a weighting system to proposed indicators/ requirements and investigate the three critical barriers. The proposed research framework would serve as a baseline for developing adaptable indicators for other categories and examining barriers to indicator implementation.

Interfacing Nanomaterials with Biology through Ligand Engineering.

Nanoparticles (NPs) have incredible potential in biology and biomedicine. Gold nanoparticles (AuNPs) have become a cornerstone of the nanomedicine revolution due to their ease of synthesis, inertness, and versatility. The widespread use of AuNPs can be traced to the development of accessible, bottom-up wet synthesis methods that emphasized the role of ligands in controlling the size, dispersity, and stability of colloids in solution. Decoration of AuNPs with organic ligands can be used to dictate the interactions of these nanomaterials with biosystems on multiple scales. The tunability of the AuNP ligand monolayer via covalent and noncovalent approaches allows the use of AuNPs in a broad range of biomedical fields.In this Account, we describe our use of AuNPs to answer a central question in the ligand engineering of colloidal nanoparticles: can we fabricate NPs that are nontoxic, modular, and functional in biological environments? We explored spherical AuNPs of different sizes and ligand structures, empirically exploring the AuNP-biomolecule interaction. We show here how the atom-by-atom control provided by organic synthesis can be used to create engineered ligands. Presenting these ligands on the surface of AuNPs creates multivalent constructs with unique and useful properties. Ligand design is a key feature of these AuNPs. We have developed ligands that have three distinct structural segments: 1) a hydrophobic alkanethiol interior that imparts stability; 2) a tetra(ethylene glycol) segment that creates a noninteracting tabula rasa surface; and 3) ligand headgroups that dictate how the AuNP interacts with the outside world. Our research into the design principles of ligands on AuNPs and their interactions with biological systems can be translated to other nanoparticle systems.This Account also summarizes the trajectory of ligand engineering in our laboratory and further afield. At the outset, experimental and theoretical fundamental studies were focused on the interactions between AuNPs and cellular components, such as proteins and lipid membranes. Understanding these behaviors provided the direction for investigating how ligands mediate the interface of AuNPs with mammalian and bacterial cells. In these experiments, it was particularly noteworthy that the ligand hydrophobicity and charge play a significant role in the uptake and toxicity of AuNPs. These revelations formed a basis for translating AuNPs to physiological environments. We present how we have integrated our synthetic abilities to construct AuNPs for biomedical applications, including delivery, bioorthogonal catalysis, antimicrobial and antitumor therapeutics, and biosensing.Overall, we hope that this Account will give the reader insight into how our research has evolved, changing AuNPs from synthetic curiosities into functional nanoplatforms for nanomedicine, all through the power of ligand design and synthesis.

Bright red frequency upconversion and blue frequency downconversion in asparagine-chelated Nd3+/Tb3+ co-doped tetragonal orthovanadate nanoparticles

A series of tetragonal LaVO4 nanoparticles (NPs) with varying concentrations of co-dopants (Nd3+/Tb3+) is synthesized using the bottom-up assembly method. Lattice dynamics and the connection between vanadium-oxygen bond distance (R) and vanadium-oxygen Raman stretching frequency (ʋ) are evidently proven using Raman analysis at 532 & 638 nm excitations. Stoke's and Anti-Stoke's emission of the developed nanophosphors are studied under 261 & 808 nm excitations respectively, and the plausible mechanism is depicted as an energy level diagram. Exchange interaction (θ ≃ 3) is obviously responsible for the cross-relaxation-induced quenching of Nd3+/Tb3+ ions. CIE coordinates of co-doped LaVO4 NPs show up in bluish and orange-red colors, extending its multi-color labeling applications.

A Novel Classification Model Using Optimal Long Short-Term Memory for Classification of COVID-19 from CT Images.

The human respiratory system is affected when an individual is infected with COVID-19, which became a global pandemic in 2020 and affected millions of people worldwide. However, accurate diagnosis of COVID-19 can be challenging due to small variations in typical and COVID-19 pneumonia, as well as the complexities involved in classifying infection regions. Currently, various deep learning (DL)-based methods are being introduced for the automatic detection of COVID-19 using computerized tomography (CT) scan images. In this paper, we propose the pelican optimization algorithm-based long short-term memory (POA-LSTM) method for classifying coronavirus using CT scan images. The data preprocessing technique is used to convert raw image data into a suitable format for subsequent steps. Here, we develop a general framework called no new U-Net (nnU-Net) for region of interest (ROI) segmentation in medical images. We apply a set of heuristic guidelines derived from the domain to systematically optimize the ROI segmentation task, which represents the dataset's key properties. Furthermore, high-resolution net (HRNet) is a standard neural network design developed for feature extraction. HRNet chooses the top-down strategy over the bottom-up method after considering the two options. It first detects the subject, generates a bounding box around the object and then estimates the relevant feature. The POA is used to minimize the subjective influence of manually selected parameters and enhance the LSTM's parameters. Thus, the POA-LSTM is used for the classification process, achieving higher performance for each performance metric such as accuracy, sensitivity, F1-score, precision, and specificity of 99%, 98.67%, 98.88%, 98.72%, and 98.43%, respectively.

ANALISA PERBANDINGAN BIAYA DAN WAKTU METODE KOSTRUKSI TOP-DOWN DAN BOTTOM-UP PADA PEKERJAAN BASEMENT GEDUNG TENTREM SEMARANG

Tentrem Semarang Building is one of the tallest buildings in Semarang with 4 basement floors and 17 top floors. The construction of basements in high-rise buildings is currently being carried out a lot, one of the factors is due to the limited land to be built, in general, the construction of basements is used for parking lots. The basement to be built has different depths and conditions, so it takes the right method according to the conditions in the field. There are 2 methods for implementing basement construction, namely bottom-up and top-down. In the basement structure work of the existing Tentrem Semarang Building construction project, the basement construction used the bottom-up construction method. In this study, the method will be modified using the top-down construction method. Information regarding work methods, worker productivity was obtained by interviewing field supervisors from the owner, interviews with supervisors from the main contractor and direct field observations. Calculation of costs and time is carried out by analyzing the capacity and productivity of labor and heavy equipment, comparing the normal time with the time after changes in construction methods. The results of the modification of the construction method using the top-down construction method for the basement structure of the Tentrem Semarang Building project took 510 days to complete and cost IDR 205,059,861,826. Whereas for the bottom-up method it takes 685 days with a total cost of IDR 253,609,405,225. The application of the Top-Down method to the implementation of the basement structure of the Tentrem Semarang building project is more effective and efficient than the implementation of the Bottom-Up method and produces a time effectiveness value of 25.55% and a cost efficiency of 19.14% compared to the Bottom-Up method

Nanocrystal Array Engineering and Optoelectronic Applications of Organic Small-Molecule Semiconductors.

Organic small-molecule semiconductor materials have attracted extensive attention because of their excellent properties. Due to the randomness of crystal orientation and growth location, however, the preparation of continuous and highly ordered organic small-molecule semiconductor nanocrystal arrays still face more challenges. Compared to organic macromolecules, organic small molecules exhibit better crystallinity, and therefore, they exhibit better semiconductor performance. The formation of organic small-molecule crystals relies heavily on weak interactions such as hydrogen bonds, van der Waals forces, and π-π interactions, which are very sensitive to external stimuli such as mechanical forces, high temperatures, and organic solvents. Therefore, nanocrystal array engineering is more flexible than that of the inorganic materials. In addition, nanocrystal array engineering is a key step towards practical application. To resolve this problem, many conventional nanocrystal array preparation methods have been developed, such as spin coating, etc. In this review, the typical and recent progress of nanocrystal array engineering are summarized. It is the typical and recent innovations that the array of nanocrystal array engineering can be patterned on the substrate through top-down, bottom-up, self-assembly, and crystallization methods, and it can also be patterned by constructing a series of microscopic structures. Finally, various multifunctional and emerging applications based on organic small-molecule semiconductor nanocrystal arrays are introduced.

On the influence of enthalpy of formation on lattice distortion and intrinsic ductility of concentrated refractory alloys

Valence electron concentration (VEC), atomic size difference (δ), and Pugh’s ratio (B/G) are a few of the empirical parameters widely used to design ductile refractory alloys. Here, we used the intrinsic ductility parameter (D), which is the ratio of surface energy (γs) and unstable stacking fault energy (γusfe), to design ductile refractory alloy. We found that the D correctly captures the experimentally observed ductility in concentrated refractory alloys. Here, we studied the enthalpy of formation (ΔEf), lattice distortion, and D of 9 refractory metals and 36 equiatomic refractory alloys using density functional theory simulations. We found that the ΔEf strongly influences the D of concentrated refractory alloys. The positive ΔEf and δ lead to large lattice distortion in concentrated refractory alloys. However, we did not find a strong correlation between lattice distortion and D in the presently studied alloys. We found that the success of VEC and Pugh’s ratio in designing ductile refractory alloys has a strong dependence on the underlying ΔEf of the alloy. We have developed a bottom-up method, which drastically reduces the number of alloys to be studied, to design ductile concentrated refractory alloys that can be thermodynamically stable.

Quantifying strong point sources emissions of CO2 using spaceborne LiDAR: Method development and potential analysis

Accurate reporting of point source emissions of CO2 is fundamental to addressing climate change. Currently, bottom-up verification methods based on inventory statistics face significant challenges in this area. Satellite remote sensing has emerged as a promising approach for cost-effective global-scale verification of point source emissions, with spaceborne LiDAR offering high spatial resolution ideal for this purpose. However, the inversion of CO2 emissions from spaceborne LiDAR CO2 concentration observations requires urgent attention, as existing methods heavily rely on prior information in diffusion models and the accuracy of meteorological data. In this work, a novel emission inversion method based on genetic algorithms and trust-region techniques is proposed to estimate CO2 emissions from point sources using spaceborne LiDAR observations. A comparison between the CO2 emission rates calculated from actual airborne LiDAR data (as a prototype of spaceborne LiDAR) and emission inventories for the Suizhong power plant showed a deviation of less than 7.0%. Observing system simulation experiment (OSSE) demonstrated that using DQ-1 (spaceborne LiDAR) observation data as input, the relative error of emission rates would be less than 0.6% when the distance between the emission source and the observation footprint is less than 10 km. Furthermore, the developed model mitigates the impact of uncertainties in meteorological data and IPDA (Integrated-Path Differential Absorption) LiDAR measurements on the final emission quantification. The proposed approach is expected to enable DQ-1 to provide affordable and accurate carbon verification services for over 20.0% of the world's strong point source emissions.