3D Structure Research Articles

Two Thiophene-Functionalized Co-MOFs as Green Heterogeneous Catalysts for the Biginelli Reaction.

Two Co(II) metal-organic frameworks (Co-MOFs), namely, [Co(DMTDC)(bimb)]n (Co-MOF-1) and {[Co(DPTDC)(bimb)(H2O)]·2DMF}n (Co-MOF-2) (H2DMTDC = 3,4-dimethylthieno[2,3-b]thiophene-2,5-dicarboxylic acid, H2DPTDC = 3,4-diphenylthieno[2,3-b]thiophene-2,5-dicarboxylic acid, bimb = 1,4-bis((1H-imidazol-1-yl)methyl)benzene), were obtained by the reaction of flexible N-containing ligand bimb and two structurally related thiophene-containing ligands H2DMTDC and H2DPTDC, respectively. These Co-MOFs displayed a 3D framework and porous structure, respectively. Co-MOF-1 and the activated sample Co-MOF-2' could act as green heterogeneous catalysts for the one-pot multicomponent Biginelli reaction, specifically the dehydration condensation process involving aldehydes, acetoacetates, and urea to yield dihydropyrimidin-2(1H)-ones. The reaction has advantages such as solvent-free conditions, water as only byproduct, readily accessible starting materials, excellent functional group compatibility, and simple operation. Both catalysts exhibited a wide substrate scope and maintained significant catalytic activity over five cycles. The special catalytic performance may be ascribed to functional groups within the ligand.

Etch characteristics of maskless oxide/nitride/oxide/nitride (ONON) stacked structure using C4H2F6-based gas

Oxide/Nitride/Oxide/Nitride (ONON; SiO2/SiNx/SiO2/SiNx) stacked structure is widely used in the 3D vertical structure of semiconductor cells. Previously, to form a 3D cells, photoresist (PR) was patterned and repeatedly trimmed on the top of ONON after the etching of one ON layer. Due to the time-consuming process of etching layer-by-layer of ON layer, two-step etch processing using C4F8-based or C4F6-based gases composed of maskless ONON stack feature etching and followed one ON layer-by layer etching by PR trimming in the ONON stack feature are employed these days. However, the two-step etching method resulted in poor etch profiles of maskless ONON stack feature in addition to high global warming potential of C4F8 and C4F6. In this study, we investigated the etching of maskless ONON stack feature using C4H2F6-based gas having a low global warming potential and the effects of C4H2F6-based gas on the etch characteristics of maskless ONON stack feature such as etch rate, etch profile, change in critical dimensional (CD), and etch selectivity between SiO2 and SiNx have been investigated. C4H2F6-based gas showed the highest etch rates compared to C4F6 and C4F8-based gases in addition to the etch selectivity of ~ 1:1 between SiO2 and SiNx due to hydrogen included in the gas structure. In addition, the change in horizontal CD was lower in the order of C4H2F6, C4F6, and C4F8-based gases due to the more effective sidewall passivation in the order of C4F8, C4F6, and C4H2F6-based gases. The thicker carbon-based polymer layer on the sidewall also played an important role in maintaining the shape of the top edge shape of maskless ONON stack feature when etching a line feature in an environment without a mask.

Improving coniferous forests leaf area index estimation by filling the occluded point cloud from airborne laser scanning

Accurate estimation of Leaf Area Index (LAI) is pivotal for understanding vegetation dynamics and ecosystem productivity. Light Detection and Ranging (LiDAR), with its capability to directly capture the three-dimensional (3D) structure of vegetation, offers a promising method for LAI estimation. However, dense leaves, especially coniferous forest, can significantly occlude the penetration of laser beams during Airborne Laser Scanning (ALS) as they traverse the canopy from above. This occlusion results in incomplete point cloud, leading to underestimation of LAI due to the omission of some leaves. Considering the characteristic of leaf clumping growth in coniferous vegetation, a method based on voxelizing point cloud and modeling correlations among neighborhood voxels was developed in this study to fill the occluded point cloud at plot level. The proposed method was validated by applying it to Loblolly pine (Pinus taeda L.) plots. Validation against field-measured LAI demonstrates that the method proposed for filling the occluded point cloud markedly enhances LAI retrieval accuracy, achieving an R2 of 0.7069 and an RMSE of 0.4760. In contrast, LAI retrieval using the original canopy point cloud yielded an R2 of 0.2835 and an RMSE of 1.5883. This advancement highlights a shift from focusing on enhancing retrieval algorithms to addressing data completeness and reliability, offering new perspectives for LAI retrieval research.

Obtaining 3D Atomic Reconstructions from Electron Microscopy Images Using a Bayesian Genetic Algorithm: Possibilities, Insights, and Limitations.

The Bayesian genetic algorithm (BGA) is a powerful tool to reconstruct the 3D structure of mono-atomic single-crystalline metallic nanoparticles imaged using annular dark field scanning transmission electron microscopy. The number of atoms in a projected atomic column in the image is used as input to obtain an accurate and atomically precise reconstruction of the nanoparticle, taking prior knowledge and the finite precision of atom counting into account. However, as the number of parameters required to describe a nanoparticle with atomic detail rises quickly with the size of the studied particle, the computational costs of the BGA rise to prohibitively expensive levels. In this study, we investigate these computational costs and propose methods and control parameters for efficient application of the algorithm to nanoparticles of at least up to 10 nm in size.

Tailoring the Li+ Intercalation Energy of Carbon Nanocage Anodes Via Atomic Al-Doping for High-Performance Lithium-Ion Batteries.

Graphitic carbon materials are widely used in lithium-ion batteries (LIBs) due to their stability and high conductivity. However, graphite anodes have low specific capacity and degrade over time, limiting their application. To meet advanced energy storage needs, high-performance graphitic carbon materials are required. Enhancing the electrochemical performance of carbon materials can be achieved through boron and nitrogen doping and incorporating 3D structures such as carbon nanocages (CNCs). In this study, aluminum (Al) is introduced into CNC lattices via chemical vapor deposition (CVD). The hollow structure of CNCs enables fast electrolyte penetration. Density functional theory (DFT) calculations show that Al doping lowers the intercalation energy of Li+. The Al-boron (B)-nitrogen (N-doped CNC (AlBN-CNC) anode demonstrates an ultrahigh rate capacity (≈300 mAhg-1 at 10 Ag-1) and a prolonged fast-charging lifespan (862.82 mAhg-1 at 5 Ag-1 after 1000 cycles), surpassing the N-doped or BN-doped CNCs. Al doping improves charging kinetics and structural stability. Surprisingly, AlBN-CNCs exhibit increased capacity upon cycling due to enlarged graphitic interlayer spacing. Characterization of graphitic nanostructures confirms that Al doping effectively tailors and enhances their electrochemical properties, providing a new strategy for high-capacity, fast-charging graphitic carbon anode materials for next-generation LIBs.

New Insights on the Glyphosate-Degrading Enzymes C-P Lyase and Glyphosate Oxidoreductase Based on Bioinformatics

Bioremediation, the degradation of environmental pollutants by living organisms, has immense potential to lead to a greener planet. Bioinformatics analysis can contribute to the identification of novel microorganisms, which biodegrade contaminants, or of participating proteins and enzymes, and the elucidation of the complex metabolic pathways involved. In this study, we focus on C-P lyase and glyphosate oxidoreductase (Gox), two enzymes which degrade glyphosate, a widely used pesticide. Amino acid sequences of the two enzymes were collected from a broad range of microorganisms using the KEGG database and BLAST. Based on this, we identified additional lineages, with putative glyphosate-degrading activity, for which no glyphosate-degrading species have been reported yet. The conserved residues in each enzyme were identified via multiple alignments and mapped onto the 3D structures of the enzymes, using PyMOL, leading to novel insights into their function. As the experimental structure of Gox is still unknown, we created structural models based on three different programs and compared the results. This approach can be used to yield insights into the characteristics of potential glyphosate-degrading enzymes. Given the limited information available, such a step is important to gain further knowledge about them, which can contribute to their application in bioremediation in the future.

HERMES: Holographic Equivariant neuRal network model for Mutational Effect and Stability prediction.

Predicting the stability and fitness effects of amino-acid mutations in proteins is a cornerstone of biological discovery and engineering. Various experimental techniques have been developed to measure mutational effects, providing us with extensive datasets across a diverse range of proteins. By training on these data, machine learning approaches have advanced significantly in predicting mutational effects. Here, we introduce HERMES, a 3D rotationally equivariant structure-based neural network model for mutation effect prediction. Pre-trained to predict amino-acid propensities from their surrounding 3D structure atomic environments, HERMES can be efficiently fine-tuned to predict mutational effects, thanks to its symmetry-aware parameterization of the output space. Benchmarking against other models demonstrates that HERMES often outperforms or matches their performance in predicting mutation effects on stability, binding, and fitness, using either computationally or experimentally resolved protein structures. HERMES offers a versatile suit of tools for evaluating mutation effects and can be easily fine-tuned for specific predictive objectives using our open-source code.

Structural modification of mesoporous lanthanum oxide into 3D coral-like and nano needle-like structure for effective broadband microwave absorbing materials☆

Structural modification of three dimensional (3D) materials for the application of dielectric loss-based microwave absorbing materials (MAMs) usually relies on intricate synthesis process and can pose challenges in terms of scalability and mass production for practical application. In this work, we reported a successful attempt in modifying the 3D structure of mesoporous lanthanum oxide (La2O3) for effective broadband MAMs candidate via simple co-precipitation process. The inclusion of cetyl-trimethylammonium bromide (CTAB) and hydrothermal aging treatment result in a significant transformation of La2O3 particles from their original polygonal form to a 3D coral-like and nano needle-like structure. The utilization of CTAB and hydrothermal aging results in the increase of surface area and a two-fold increase in pore volume of the resulting La2O3. Due to its unique 3D structure, the 3D coral-like and nano needle-like La2O3 materials possess a broadband electromagnetic (EM) wave absorption characteristic with the effective absorption bandwidth (EAB) covering the C-band frequency range. Specifically, in the La2O3 C-H sample (with CTAB – with hydrothermal), it exhibits strong EM wave absorption with a reflection loss (RL) value of –33.07 dB which equals to 99.95% EM wave absorption at a thickness of only 1.50 mm. The detailed analysis of EM wave absorption properties reveals that the improvement of La2O3 materials to attenuate EM wave energy arises from the dielectric loss phenomenon, the enhanced interfacial polarization, multiple reflections mechanism, and conduction loss mechanism induced by the 3D structural formation of the La2O3 structure. This work proposes a novel and efficient approach in synthesizing and modifying 3D materials for effective broadband EM wave absorption.

Characterisation of C-C Ligand 7 (CCL7) as Asthma Genetic Marker in Pigtailed Monkey.

The pigtailed monkey (Macaca nemestrina) is one of the species that have potency like the cynomolgus monkey that is widely used as an animal model for asthma study. The CCL7 gene has potential as a genetic marker because of the secreted chemokine that plays a role in asthma. The aims of this research are to characterise the CCL7 gene of pigtailed monkey, compare the structure of their CCL7 gene with other primate species and determine model 3D structure protein prediction of CCL7 protein. The amplicons were sequenced, and the results were analysed by the bioinformatics technique. The 3D CCL7 protein structure was predicted using I-TASSER. We have isolated 2221 bp sequences CCL7 gene and 109 amino acids from pigtailed monkey. Variation of CCL7 gene sequence between pigtailed monkey and other primate species (Macaca fascicularis, M. mulatta and Homo sapiens) was found in exon 1, exon 2 and exon 3 as CDS (Coding DNA Sequence) region. The analysis homology of nucleotides and amino acid sequences of the CCL7 gene indicated that the pigtailed monkey and three other primate species have a high homology rate with an identity score above 90%. Meanwhile, a comparative analysis of CDS and amino acid regions showed that the pigtailed monkey also has the highest similarity with the three other primate species with more than 90% identity score. The 3D structure protein prediction model of the CCL7 pigtailed monkey revealed the highest similarity with H. sapiens with an identity value of about 95%. Therefore, the pigtailed monkey CCL7 gene has high similarity with H. sapiens, which means that based on molecular similarity, the pigtailed monkey has the potential to be an animal model for asthma study, especially the study of molecular and the role of CCL7 in asthma pathogenesis.

Nonparametric Bayesian reconstruction of Galactic magnetic fields using information field theory

Context. Ultrahigh-energy cosmic rays (UHECRs) are charged particles with energies surpassing 1018 eV. Their sources remain elusive because they are obscured by deflections caused by the Galactic magnetic field (GMF). This challenge is further complicated by our limited understanding of the 3D structure of the GMF because current GMF observations primarily consist of quantities that are integrated along the line of sight (LOS). Nevertheless, data from upcoming stellar polarization surveys along with Gaia stellar parallax data are expected to yield local GMF measurements. Aims. This study is the second entry in our exploration of a Bayesian inference approach to the local GMF that uses synthetic local GMF observations that emulate forthcoming local GMF measurements, and attempts to use them to reconstruct its 3D structure. The ultimate aim is to trace back observed UHECRs and thereby update our knowledge about their possible origin. Methods. In this proof-of-concept work, we assumed as ground truth a magnetic field produced by a dynamo simulation of the Galactic ISM. We employed methods of Bayesian statistical inference in order to sample the posterior distribution of the GMF within part of the Galaxy. By assuming a known rigidity and arrival direction of an UHECR, we traced its trajectory back through various GMF configurations drawn from the posterior distribution. Our objective was to rigorously evaluate the performance of our algorithm in scenarios that closely mirror the setting of expected future applications. In pursuit of this, we conditioned the posterior to synthetically integrated LOS measurements of the GMF, in addition to synthetic local plane of sky-component measurements. Results. Our results demonstrate that for all locations of the observed arrival direction on the plane of sky, our algorithm is able to substantially update our knowledge on the original arrival direction of UHECRs with a rigidity of E/Z = 5 × 1019 eV, even without any LOS information. When the integrated data are included in the inference, the regions of the celestial sphere in which the maximum error occurs are greatly reduced. The maximum error is diminished by a factor of about 3 even in these regions in the specific setting we studied. Additionally, we are able to identify the regions in which the largest error is expected to occur.

Investigation of Lysophospholipids-DHA transport across an in vitro human model of blood brain barrier

Several studies emphasized on the preventive and therapeutic potential of Docosahexaenoic Acid (DHA, 22:6n-3) supplementation in chronic and age-related disorders including neurodegenerative diseases. Researchers principally studied the cerebral accretion of Lysophosphatidylcholine (LysoPC-DHA), the furthermost vital Lysophospholipid-DHA (LysoPL-DHA) in blood plasma. Nevertheless, the cerebral bioavailability of other LysoPL-DHA forms including Lysophosphatidylethanolamine (LysoPE-DHA), and Lysophosphatidylserine (LysoPS-DHA) were not extensively examined even though their vital biological functions in the brain. Hence, the aim of the present study was to evaluate the toxicity and transport of DHA in comparison to several LysoPL-DHA including LysoPC-DHA, LysoPE-DHA and LysoPS-DHA across a human model of blood-brain barrier (BBB). The human brain-like endothelial cells (hBLECs) monolayer tightness was evaluated by the parallel assessment of the permeability of fluorescent marker Lucifer yellow (LY) and revealed the absence of toxicity of non-esterified DHA and all LysoPL-DHA towards hBLECs. LysoPC-DHA, LysoPE-DHA and LysoPS-DHA displayed a higher recovery in the abluminal medium in comparison to non-esterified DHA at 30, 60 and 120 min post-incubation. Among all, LysoPS-DHA revealed the highest apparent coefficient permeability (Papp) 85.87 ± 4.24 x 10−6 cm s−1 and was significantly different than DHA, LysoPC-DHA and LysoPE-DHA. More interestingly, when studying the time course of Papp of DHA, LysoPC-DHA and LysoPE-DHA, at different post-incubation time, this permeability decreases with time especially for LysoPC-DHA and LysoPE-DHA, not for DHA. Furthermore, LysoPS-DHA exhibited the highest intracellular accumulation (10.39 ± 0.49 %) in hBLECs in comparison to all other tested lipids. Finally, differences in 3D structures and molecular electrostatic potential maps calculation of LysoPL-DHA could explain the dissimilar cerebral uptake of LysoPL-DHA. Altogether, our findings raise the novel hypothesis that LysoPS-DHA may represent a preferred physiological carrier of DHA to the brain.

Naphthyl-functionalized acetamide derivatives: Promising agents for cholinesterase inhibition and antioxidant therapy in Alzheimer’s disease

This study presents the synthesis and characterization of a series of 13 novel acetamides. These were subjected to Ellman’s assay to determine the efficacy of the AChE and BChE inhibitors. Finally, we report their antioxidant activity as an alternative approach for the search for drugs to treat AD. These studies revealed that compounds 1a–1k and 2l–2m were obtained in moderate yield. Four amides (1h, 1j, 1k, and 2l) were selective for one of the enzymes (BChE); thus, those that inhibited BChE were more active than the positive control (galantamine) and showed better IC50 values (3.30–5.03 µM). The theoretical free binding energies calculated by MM-GBSA indicated that all inhibitors were more stable than rivastigmine, and the inhibition mechanisms involved the entire active site: peripheral anionic site, oxyanion hole, acyl-binding pockets, and catalytic site. We examined the cytotoxicity of compounds 1h, 1j, 1k, and 2l in human dermal cells and found that they did not exhibit any toxic effects under the tested conditions. Additionally, these compounds, which also inhibited BChE, displayed mixed inhibition and did not exhibit hemolytic effects on human erythrocytes. Furthermore, the ABTS and DPPH assays indicated that, although none of the compounds showed activity in the DPPH assay, the EC50 values for radical trapping by the ABTS method showed that compounds 1a, 1d, 1e, and 1g had EC50 values lower than 10 µg/mL, indicating their strong radical scavenging capacity. We also report the crystal structures of compounds 1c, 1d, 1f, and 1g, which are found in monoclinic crystal systems.

Effect of 3D printing accuracy by wheat starch gel combined with canola oil

This study investigated the impact of canola oil (CO) on wheat starch (WS) in 3D printing, focusing on intermolecular interactions and gel characteristics. 3D printing performance, microstructure, rheology, texture, proton distribution, thermal properties, molecular structures, and crystal structure were used to analyze the properties of starch gels. The results showed that adding CO improved the similarity between printed products and their models, and facilitated smoother extrusion by enhancing the cohesiveness and adhesiveness of the WS-CO complex. Moreover, adding >6 % CO to the starch gels destabilized the 3D-printed structure, causing the top layer to collapse, which affected the aesthetics and printing accuracy. The WS-4%CO complex demonstrated superior printing performance and enhanced accuracy. FTIR (Fourier Transform Infrared) and NMR (Nuclear Magnetic Resonance) results indicated that the interaction between WS and CO involved non-covalent interactions, primarily hydrophobic interactions. SEM (scanning electron microscope) results further revealed the molecular structure, CO hindered the interaction between amylose and amylopectin, disrupting the ordered structure and increasing lamellar formation, thereby affecting the stability of the 3D printing structure. This experiment provides valuable insights for enriching the nutrition and improving the accuracy of 3D-printed food.

Artificial intelligence-aided semi-automatic joint trace detection from textured three-dimensional models of rock mass

It is of great importance to obtain precise trace data, as traces are frequently the sole visible and measurable parameter in most outcrops. The manual recognition and detection of traces on high-resolution three-dimensional (3D) models are relatively straightforward but time-consuming. One potential solution to enhance this process is to use machine learning algorithms to detect the 3D traces. In this study, a unique pixel-wise texture mapper algorithm generates a dense point cloud representation of an outcrop with the precise resolution of the original textured three-dimensional (3D) model. A virtual digital image rendering was then employed to capture virtual images of selected regions. This technique helps to overcome limitations caused by the surface morphology of the rock mass, such as restricted access, lighting conditions, and shading effects. After AI-powered trace detection on two-dimensional (2D) images, a 3D data structuring technique was applied to the selected trace pixels. In the 3D data structuring, the trace data were structured through 2D thinning, 3D reprojection, clustering, segmentation, and segment linking. Finally, the linked segments were exported as 3D polylines, with each polyline in the output corresponding to a trace. The efficacy of the proposed method was assessed using a 3D model of a real-world case study, which was used to compare the results of artificial intelligence (AI)-aided and human intelligence trace detection. Rosette diagrams, which visualize the distribution of trace orientations, confirmed the high similarity between the automatically and manually generated trace maps. In conclusion, the proposed semi-automatic method was easy to use, fast, and accurate in detecting the dominant jointing system of the rock mass.

Long-term three-dimensional high-resolution imaging of live unlabeled small intestinal organoids via low-coherence holotomography

Organoids, which are miniature in vitro versions of organs, possess significant potential for studying human diseases and elucidating their underlying mechanisms. Live imaging techniques play a crucial role in organoid research and contribute to elucidating the complex structure and dynamic biological phenomena of organoids. However, live, unlabeled high-resolution imaging of native organoids is challenging, primarily owing to the complexities of sample handling and optical scattering inherent in three-dimensional (3D) structures. Additionally, conventional imaging methods fail to capture the real-time dynamic processes of growing organoids. In this study, we introduce low-coherence holotomography as an advanced, label-free, quantitative imaging modality designed to overcome several technical obstacles for long-term live imaging of 3D organoids. We demonstrate the efficacy of low-coherence holotomography by capturing high-resolution morphological details and dynamic activities within mouse small intestinal organoids at subcellular resolution. Moreover, our approach facilitates the distinction between viable and nonviable organoids, significantly enhancing its utility in organoid-based research. This advancement underscores the critical role of live imaging in organoid studies, offering a more comprehensive understanding of these complex systems.

Determination of multiple restricted and prohibited ingredients in hair dyes using a novel 3D stationary phase coupled with gas chromatography-mass spectrometry

Hair dyes (HDs) are mainly composed of various benzene series, amines, and phenolic compounds. These ingredients are well known to have allergenic, teratogenic, and carcinogenic properties. As such, the presence of these ingredients in HDs has received increased attention in recent years. At present, the applications of traditional analytical and detection methods and commercial chromatographic columns are limited by problems such as poor qualitative analysis and inaccurate quantification. Thus, the development of new analytical and detection technologies and stationary phases is an urgent endeavor. Moreover, HDs contain complex compounds and exhibit significant matrix interference. Hence, appropriate sample pretreatment methods are necessary to analyze HDs. In this study, the 3D nonpolar rigid structure of triptycene (TP) was combined with the polar flexible chains of polyethylene glycol (PEG) to design and synthesize a TP derivative, TP-PEG, as a stationary phase for chromatographic columns. The stationary phase enabled the expansion of the selection range for polar and nonpolar analytes. Subsequently, gas chromatography-mass spectrometry (GC-MS) was used to quantitatively analyze 22 ingredients in HDs. The experimental results demonstrated that analytes with different polarities exhibited sharp and symmetrical peak shapes on the stationary phase, and all 22 analytes achieved baseline separation on the chromatographic column. The 22 ingredients in HDs showed good linear relationships within their respective ranges, with correlation coefficients greater than 0.9985. The average recovery rates at three spiked levels were in the range of 89.2%-103.2%, and RSDs were less than 5%. Compared with traditional methods, the proposed method has higher efficiency and better accuracy, thus verifying the excellent separation performance of the new stationary phase and the effectiveness of the established GC-MS detection method. The findings indicated the applicability of the developed method to the detection and analysis of various compounds in HDs.

Synergistic Effects of Psidium Guajava and Copper Nanoparticles Reinforced Hybrid Hydrogel for Tissue Engineering

Hydrogels are biopolymers proficient in engrossing much water in their 3D network structure. However, single-polymer hydrogels frequently experience poor physio-mechanical properties, confining their border applications. The present work concentrated on developing chemically crosslinked hydrogels using the terpolymerization of gelatin (GEL), guar gum (GGM), and polyvinyl alcohol (PVA). Ethanolic extract of Psidium guajava leaf (EPG) and copper nanoparticles (CuNPs) were added to enhance the biomechanical properties of the developed hydrogels. Hydrogels' viscoelastic, mechanical, swelling, and cytotoxicity properties were assessed. All the hydrogels exhibited a porous-like structure with a swelling index of 230 to 280%. A compressive strength of 5 MPa with splendid chondrocyte viability was noticed in the hydrogels comprised of EPG and CuNPs. The multiple interactions among the polymer chains impart better frequency and shear strain-dependent behavior. The time-dependent frictional behavior of hydrogel under the lubrication of artificial synovial fluid reveals the decreased coefficient of friction over time. The performance of the hybrid hydrogel enhanced with EPG and CuNPs was superior, making it a promising material for tissue engineering applications.

The 3D Density Structure of the South China Sea Based on Wavelet Multi-Scale Analysis of Gravity Data and Its Tectonic Implications

Due to its unique geographical location and complex geological evolution processes, the South China Sea has been a focus of extensive research. Previous studies on the density structure of the South China Sea mostly focused on 2D density structures, with relatively limited research on 3D density structures. A comprehensive study is still needed to refine the expansion mechanism and tectonic evolution of the South China Sea. In this study, we utilized wavelet multi-scale analysis of gravity data to obtain a 3D density model of the South China Sea and discussed its tectonic evolution from the pattern of density anomalies. The inversion results show that (1) the expansion of the South China Sea caused the typical thin oceanic crust and parts of the continent–ocean transition zone may fracture due to the expansion; (2) the low-density anomaly in the upper mantle of Luzon Island may indicate partial melting or the upwelling of asthenosphere materials; and (3) the expansion of the South China Sea is influenced by multiple plate forces and uneven forces affect the distribution of high-density anomalies in the upper mantle.

Structure Prediction of Large RNAs with AlphaFold3 Highlights its Capabilities and Limitations

DeepMind’s AlphaFold3 webserver offers exciting new opportunities to make structural predictions of heterogeneous macromolecular systems. Here we attempt to apply AlphaFold3 to large RNA molecules whose 3D atomic structures are unknown but whose physical dimensions have been studied experimentally. One difficulty that we encounter is that models returned by AlphaFold3 often contain severe steric clashes and, less frequently, clear breaks in the phosphodiester backbone, with the probability of both events increasing with the length of the RNA. Restricting attention to those RNAs for which non-clashing models can be obtained, we find that hydrodynamic radii computed from the AlphaFold3 models are much larger than those reported experimentally under low salt conditions but are in better agreement with those reported in the presence of polyvalent cations. For two RNAs whose shapes have been imaged experimentally, the computed anisotropies of the AlphaFold3-predicted structures are too low, indicating that they are excessively spherical; extending this analysis to larger RNAs shows that they become progressively more spherical with increasing length. Overall, the results suggest that AlphaFold3 is capable of producing plausible models for RNAs up to ∼2000 nucleotides in length, but that thousands of predictions may be required to obtain models free of geometric problems.

High-capacity and long-life Fe2O3/MOFs composite anodes: Preparation, characterization, morphology regulation and electrochemical performance

Among the various transition metal oxides used in lithium-ion battery anodes, iron oxide (Fe2O3) has received extensive attention due to its low cost, good environmental compatibility and high theoretical capacity. However, the volume expansion and structural collapse during the charging-discharging cycling process significantly limited the practical application of Fe2O3. Herein, we demonstrate enhanced electrochemical properties and stability of Fe2O3 by anchoring it on metal-organic frameworks (MOFs). Through adjusting the MOF ligands and introducing humic acid (HA), we reached rational control of the morphology for the Fe2O3/MOF composites. The optimized sample, namely Fe2O3@HA-Fe-ATA, shows 3D cauliflower-like structure with abundant pores, which are beneficial to the active sites exposure, electrolyte penetration, as well as the tolerance toward volume expansion. Consequently, high reversible capacity of 1442.7 mAh g−1 (at 0.1 A g−1), excellent rate performance (<11 % capacity decrease when current density increases from 0.1 A g−1 to 1 A g−1), and satisfactory cycle stability (91 % capacity retention after 500 cycles at 1 A g−1) are obtained. This work demonstrates that the rational design of MOF by introduction of appropriate ligand is a very efficient approach to optimize the electrochemical properties of the resultant electrode material.