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33 results for “Potential Density”
Density independent prey choice, taxonomy, life history and web characteristics determine the diet and biocontrol potential of spiders (Linyphiidae and Lycosidae) in cereal crops - Dataset
<p>Materials and Methods</p> <p>Fieldwork</p> <p>Money spiders (Araneae: Linyphiidae) and wolf spiders (Araneae: Lycosidae) were the two most common families present in these field surveys, so were prioritised for collection. Spiders were visually located along transects in two adjacent barley fields at Burdons Farm, Wenvoe in South Wales (51°26'24.8"N, 3°16'17.9"W) and collected from occupied webs and the ground, between April and September 2018. Surveys and sampling were conducted five days per week across this period. Each transect was adjacent to a randomly selected tramline and they were distributed across the entire field. The areas searched were 4 m<sup>2</sup> quadrats at least 10 m apart and all observed linyphiids and lycosids were collected in approximately 15-minute searches. The spiders included in this study were taken from 64 locations across 24 days (Supplementary Table 3) along the aforementioned transects. Spiders were individually placed into 1.5 ml microcentrifuge tubes containing 100 % ethanol using an aspirator, regularly changing meshing, at least every five spiders, to limit potential cross-contamination between spiders (spiders were also subsequently washed during transferral to fresh ethanol at the identification and, separately, dissection stages). Linyphiids occupying webs were prioritised for collection, but ground-active linyphiid spiders were also collected. For each spider taken from a web, the height of the web from the ground and its approximate dimensions were recorded, the latter calculated as approximate web area. Spiders were taken to Cardiff University, transferred to fresh ethanol, adults identified to species-level and juveniles to genus, and stored at -80 °C in 100 % ethanol until subsequent DNA extraction. To obtain data on local prey density, 4 m<sup>2</sup> of ground and crop stems were suction sampled using a ‘G-vac’ for 30 seconds at each quadrat from which spiders were collected, with the collected material emptied into a bag, any organisms immediately killed with ethyl-acetate and material frozen for storage before sorting into 70 % ethanol in the lab.</p> <p>All invertebrates were identified to family level due to the restriction of many of the metabarcoding-derived dietary data to this level, and the difficulty associated with finer taxonomic resolution of many taxa. Exceptions included springtails of the superfamily Sminthuroidea (Sminthuridae and Bourletiellidae, which were often indistinguishable following suction sampling and preservation due to the fine features necessary to distinguish them) which were left at super-family, mites (many of which were immature or in poor condition) which were identified to order level and wasps of the superfamily Ichneumonoidea (which were identified no further due to obscurity of wing venation due to damage).</p> <p> </p> <p>Extraction and high-throughput sequencing of spider gut DNA</p> <p>Given their prevalence in field collections, dietary analysis was carried out for the linyphiid genera <em>Erigone</em>, <em>Tenuiphantes</em>, <em>Bathyphantes</em> and <em>Microlinyphia </em>(Araneae: Linyphiidae), and the Lycosidae genus <em>Pardosa</em>. Spiders were transferred to and washed in fresh 100 % ethanol to reduce external contaminants prior to identification via morphological key <sup>1</sup>. Abdomens were removed from spiders and again washed in and transferred to fresh 100 % ethanol. DNA was extracted from the abdomens via Qiagen TissueLyser II and DNeasy Blood & Tissue Kit (Qiagen) as per the manufacturer protocol, but with an extended lysis time of 12 hours to account for the complex and branched gut system in spider abdomens <sup>2</sup>. At least one extraction negative (blank tubes treated identically to samples) was included per 12 spiders (each extraction typically contained 24 spiders, thus two extraction negatives), which was included in subsequent PCR and high-throughput sequencing to detect instances of lab/reagent contamination.</p> <p>For amplification of DNA, two primer pairs were used. BerenF-LuthienR <sup>3</sup> amplified a broad range of invertebrates including spiders, and TelperionF-LaureR, amplified a range of invertebrates but fewer spiders (modified from TelperionF-LaurelinR <sup>3</sup> via one base-pair change from Laurelin; 5’-ggrtawacwgttcawccagt-3’). Primers were labelled with unique 10 bp molecular identifier tags (MID-tags) so that each individual had a unique pairing of forward and reverse tags for identification of each spider post-sequencing. PCR reactions of 25 µl contained 12.5 µl Qiagen PCR Multiplex kit, 0.2 µmol (2.5 µl of 2 µM) of each primer and 5 µl template DNA. Reactions were carried out in the same thermocycler, optimised via temperature gradient, with an initial 15 minutes at 95 °C, 35 cycles of 95 °C for 30 seconds, the primer-specific annealing temperature for 90 seconds and 72 °C for 90 seconds, respectively, followed by a final extension at 72 °C for 10 minutes. BerenF-LuthienR and TelperionF-LaureR used annealing temperatures of 52 °C and 42 °C, respectively.</p> <p>Within each PCR 96-well plate, 12 negative controls (extraction and PCR), 2 blank controls and 2 positive controls were included (i.e. 80 samples per plate), based on Taberlet <em>et al. </em>(2018). Positive controls were mixtures of invertebrate DNA comprised of non-native Asiatic species in four different proportions (Supplementary Table 1) and blanks were empty wells within each plate to identify tag-jumping into unused MID-tag combinations. PCR negative controls were DNase-free water treated identically to DNA samples. A negative control was present for each MID-tag to identify any contamination of primers. All PCR products were visualised in a 2 % agarose gel with SYBRSafe (Thermo Fisher Scientific, Paisley, UK) and placed in categories based on their relative brightness. The concentration of these brightness categories was quantified via Qubit dsDNA High-sensitivity Assay Kits (Thermo Fisher Scientific, Waltham, MA, USA) with at least three representatives of each category per plate. The PCR products were then proportionally pooled according to these concentrations. Each pool was cleaned via SPRIselect beads (Beckman Coulter, Brea, USA), with a left-side size selection using a 1:1 ratio (retaining ~300-1000 bp fragments). The concentration of the pooled DNA was then determined via Qubit dsDNA High-sensitivity Assay Kits and pooled together into one library per primer pair. Library preparation for Illumina sequencing was carried out on the cleaned libraries via NEXTflex Rapid DNA-Seq Kit (Bioo Scientific, Austin, USA) and samples were sequenced on an Illumina MiSeq via a V3 chip with 300-bp paired-end reads (expected capacity ≤25,000,000 reads). Bioinformatic analysis followed (Drake et al., 2021; Supplementary Information 1).</p> <p> </p> <p>Statistical analysis</p> <p>All analyses were conducted in R v4.0.0 <sup>6</sup>. Initial multivariate analyses used binary data (i.e., presence/absence) given the various problems inherent to quantifying metabarcoding data <sup>7,8</sup>. Prey species that occurred only once across all of the dietary samples were removed before further analyses to prevent outliers skewing the results, which is particularly problematic for non-metric multidimensional scaling. Spider diets were compared between variables using multivariate generalized linear models (MGLMs) via ‘manyglm’ in the ‘mvabund’ package <sup>9</sup> with a binomial error family and Monte Carlo resampling. Model independent variables included spider genus, spider life stage (juvenile or adult, the latter defined by fully developed genitalia), spider sex and all two-way interactions between these variables. Pairwise two-way interactions were also included between the aforementioned variables and Julian day to account for how seasonality may affect these relationships.</p> <p>Coarse dietary differences were visualised by non-metric multidimensional scaling (NMDS) via metaMDS in the ‘vegan’ package <sup>10</sup> with Jaccard distance in two dimensions and 999 tries. For NMDS, outliers (usually samples containing rare taxa) were identified by plotting and subsequently removed to facilitate separation of samples and achieve minimum stress. For visualisation of the effect of categorical variables against the dietary NMDS, spider plots were created using ‘ordispider’ with ‘ggplot’ and the ‘RColorBrewer’ ‘Accent’ colour palette <sup>11</sup>. Spider diet was compared against web characteristics for spiders for which both data were available using the MGLM process outlined above, but with starting models containing web height, web area, an interaction between the two, and pairwise interactions between genus, life stage and sex with the two web variables. This model used the same binomial error family as above, but with a ‘cloglog’ link function. For visualisation of the effect of continuous variables against the NMDS, surf plots were created with scaled coloured contours using the function “ordisurf” of the “ggplot” package in R.</p> <p>All prey taxa were classified as agricultural pests, natural enemies or excluded from subsequent analyses of intraguild predation and biocontrol (Supplementary Table 2). Intraguild predation and biocontrol variables were created by counting the number of natural enemy taxa, and, separately, of agriculturally relevant “pest” taxa (taxa containing species that commonly adversely affect agricultural productivity; Supplementary Table 2) in each spider’s diet. These resultant count data (effectively the diversity of pests and natural enemies predated by each individual spider) were separately analysed against spider genus, life stage and sex via GLM. “Site” (denoting the 4 m<sup>2</sup> area from which spiders were collected within fields) was initially included as a random effect in generalized linear mixed-models, but no significant effect was observed when comparing this model against a standard GLM via a likelihood ratio test of nested models using the ‘lrtest’ command in the ‘lmtest’ package <sup>12</sup>. Standard GLMs were thus used to avoid issues relating to singularity in the mixed models. The assumptions for the resultant Poisson error family GLMs were tested using the “testResiduals” function of the ‘DHARMa’ package <sup>13</sup>. Intraguild predation and biocontrol differences between significant terms were visualised using violin plots with the quartiles, median and 95 % upper limit annotated using the ‘geom_violin’ function in ‘ggplot2’.</p> <p><em>In situ</em> spider prey choice was analysed using network-based null models in the ‘econullnetr’ package <sup>14</sup> with the ‘generate_null_net’ command, visually represented with the ‘plot_preferences’ command. Binary dietary data were used alongside suction sample count data to represent prey availability. These suction sample data, as described above, were collected at the same sites as the spiders three days after spider collection. Prior to the taxonomic prey choice analysis, an hemipteran identified no further than order level through dietary analysis was removed due to the inability to pair it to any present prey taxa with certainty. Standardised effect sizes (SES) were extracted for all comparisons for each individual spider and compared between genera, life stages and sexes using permutational multivariate analysis of variance (PerMANOVA) using the ‘adonis’ function of the ’vegan’ package with 9999 permutations and a Euclidean distance matrix to determine overall differences in prey choice.</p> <p> </p> <p>References</p> <p>1. Roberts, M. J. <em>The Spiders of Great Britain and Ireland (Compact Edition)</em>. (Harley Books, 1993).</p> <p>2. Krehenwinkel, H., Kennedy, S., Pekár, S. & Gillespie, R. G. A cost-efficient and simple protocol to enrich prey DNA from extractions of predatory arthropods for large-scale gut content analysis by Illumina sequencing. <em>Methods Ecol. Evol.</em> <strong>8</strong>, 126–134 (2017).</p> <p>3. Cuff, J. P. <em>et al.</em> Money spider dietary choice in pre- and post-harvest cereal crops using metabarcoding. <em>Ecol. Entomol.</em> <strong>46</strong>, 249–261 (2021).</p> <p>4. Taberlet, P., Bonin, A., Zinger, L. & Coissac, E. <em>Environmental DNA</em>. (Oxford University Press, 2018).</p> <p>5. Drake, L. E. <em>et al.</em> An assessment of minimum sequence copy thresholds for identifying and reducing the prevalence of artefacts in dietary metabarcoding data. <em>Methods Ecol. Evol.</em> <strong>in press</strong>, (2021).</p> <p>6. R Core Team. R: A language and environment for statistical computing. (2020).</p> <p>7. Deagle, B. E., Thomas, A. C., Shaffer, A. K. & Trites, A. W. Quantifying sequence proportions in a DNA-based diet study using Ion Torrent amplicon sequencing: which counts count? <em>Mol. Ecol. Resour.</em> <strong>13</strong>, 620–633 (2013).</p> <p>8. Deagle, B. E. <em>et al.</em> Counting with DNA in metabarcoding studies: How should we convert sequence reads to dietary data? <em>Mol. Ecol.</em> <strong>28</strong>, 391–406 (2019).</p> <p>9. Wang, Y., Naumann, U., Wright, S. T. & Warton, D. I. mvabund – an R package for model-based analysis of multivariate abundance data. <em>Methods Ecol. Evol.</em> <strong>3</strong>, 471–474 (2012).</p> <p>10. Oksanen, J. <em>et al.</em> vegan: Community Ecology Package. (2016).</p> <p>11. Neuwirth, E. RColorBrewer: ColorBrewer palettes. (2014).</p> <p>12. Zeileis, A. & Hothorn, T. Diagnostic checking in regression relationships. <em>R News</em> <strong>2</strong>, 7–10 (2002).</p> <p>13. Hartig, F. DHARMa: residual diagnostics for hierarchical (multi-level/mixed) regression models. (2020).</p> <p>14. Vaughan, I. P. <em>et al.</em> econullnetr: an r package using null models to analyse the structure of ecological networks and identify resource selection. <em>Methods Ecol. Evol.</em> <strong>9</strong>, 728–733 (2018).</p>
Electric potential and charge density for null emission
<p>Fig6(c-d) plots electric potential and charge density for null emission. Parameters: $m=5, \tau_{\rm p}=4, v_0=0, \phi_g=1$. </p>
Potential map generated by the RF spatial model to define ideal zones for the occurrence of high density of giant trees in the Amazon
<p>The provided image is a theoretical map of giant tree density in the Amazon, generated from a spatial model based on the **Random Forest** algorithm. The map displays the spatial distribution of tree density, representing the number of trees taller than 60 meters per square kilometer (trees/km²). The model was developed using climatic, topographic, and soil variables to predict areas with higher concentrations of these giant trees.</p> <p>The areas are color-coded according to different density ranges, where:<br>- Lighter shades indicate lower tree density (≤ 5 trees/km²),<br>- Darker shades indicate higher density (up to 141 trees/km²).</p> <p>Biogeographic provinces within the Amazon biome, such as the **Guiana Shield**, **Xingu-Tapajós**, and **Roraima**, are highlighted, showing distinct density patterns across the Amazon region. This map is a valuable tool for understanding the spatial distribution of giant trees in the Amazon and plays a crucial role in conservation efforts and ecological monitoring in the region.</p>
Data from: Angiosperm wood structure: global patterns in vessel anatomy and their relationship to wood density and potential conductivity
Woody stems comprise a large biological carbon fraction and determine water transport between roots and leaves; their structure and function can influence both carbon and hydrological cycles. While angiosperm wood anatomy and density determine hydraulic conductivity and mechanical strength, little is known about interrelations across many species. We compiled a global dataset comprising two anatomical traits for 3005 woody angiosperms: mean vessel lumen area ( ) and number per unit area (N). From these, we calculated vessel lumen fraction (F = N) and size/number ratio (S = /N), a new vessel composition index. We examined extent to which F and S influenced potential sapwood specific stem conductivity (KS) and wood density (D; dry mass/fresh volume). F and S varied essentially independently across angiosperms. Variation in KS was driven primarily by S, and variation in D was virtually unrelated to F and S. Tissue density outside vessel lumens (DN) must predominantly influence D. High S should confer faster Ks but incur greater freeze-thaw embolism risk. F should also affect KS, and both F and DN should influence mechanical strength, capacitance, and construction costs. Improved theory and quantification are needed to better understand ecological costs and benefits of these three distinct dimensions.
Phonons from Density-Functional Perturbation Theory using the All-Electron Full-Potential Linearized Augmented Plane-Wave Method FLEUR
<p>The archive files contain the input and result files for the corresponding publication in IOP Electronic Structure - Technical Notes, as well as a short python script to plot them.</p>
Density-dependent effects of parasitism on the activity of a benthic engineer species: potential impact on ecosystem functioning
<p>While parasitism is a common lifestyle on Earth, its importance for the functioning of marine ecosystems has been overlooked for a long time. In particular, parasites have significant potential to influence central ecological processes through their impacts on hosts that serve as ecosystem engineers. Using an ex-situ experimental approach, we explored the effects of trematode parasites on the engineering bioturbation activity of a common and abundant bivalve along European Atlantic soft-bottom coastlines, the peppery furrow shell <em>Scrobicularia</em> <em>plana</em>, as well as knock-on effects for nutrient exchanges at the sediment-water interface. Trematodes negatively impacted the host's ability to transport sediment particles and solutes in a density-dependent way with parasite burden explaining 22–31% of the inter-individual variability. This could be explained by parasitism impairing the bivalve physiological state and ability to burrow as we observed a decrease in the condition index and the burrowing depth of the bivalves with an increase in the number of parasites they host. In contrast, the influence of <em>S. plana</em> on benthic biogeochemical fluxes did not vary significantly according to parasitic burden over a short time scale. Here, we focused on the effects of trematode parasites on the sole behaviour of <em>S. plana</em> and thus excluded other macrofaunal organisms. We should next test whether trematodes modulate the structure and functioning of benthic communities dominated by <em>S. plana</em> to better understand and quantify the engineering role of parasites in soft-bottom coastal environments.</p>
Data from: Angiosperm wood structure: global patterns in vessel anatomy and their relationship to wood density and potential conductivity
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Density-dependent effects of parasitism on the activity of a benthic engineer species: potential impact on ecosystem functioning
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Data from: Clonal diversity impacts coral cover in Acropora cervicornis thickets: potential relationships between density, growth and polymorphisms
As coral reefs decline, cryptic sources of resistance and resilience to stress may be increasingly important for the persistence of these communities. Among these sources, inter‐ and intraspecific diversity remain understudied on coral reefs but extensively impact a variety of traits in other ecosystems. We use a combination of field and sequencing data at two sites in Florida and two in the Dominican Republic to examine clonal diversity and genetic differentiation of high‐ and low‐density aggregations of the threatened coral Acropora cervicornisin the Caribbean. We find that high‐density aggregations called thickets are composed of up to 30 genotypes at a single site, but 47% of genotypes are also found as isolated, discrete colonies outside these aggregations. Genet–ramet ratios are comparable for thickets (0.636) and isolated colonies after rarefaction (0.569), suggesting the composition of each aggregation is not substantially different and highlighting interactions between colonies as a potential influence on structure. There are no differences in growth rate, but a significant positive correlation between genotypic diversity and coral cover, which may be due to the influence of interactions between colonies on survivorship or fragment retention during asexual reproduction. Many polymorphisms distinguish isolated colonies from thickets despite the shared genotypes found here, including putative nonsynonymous mutations that change amino acid sequence in 25 loci. These results highlight intraspecific diversity as a density‐dependent factor that may impact traits important for the structure and function of coral reefs.
Data from: Inter-specific variation in the potential for upland rush management advocated by agri-environment schemes to increase breeding wader densities
<p>Encroachment of rush Juncus spp. in the United Kingdom uplands poses a threat to declining wader populations due to taller, denser swards that can limit foraging and breeding habitat quality for some species. Rush management via cutting, implemented through agri-environment schemes (AESs), could thus increase wader abundance, but there is insufficient assessment and understanding of how rush management influences upland waders. Across two upland regions of England [South West Peak (SWP) and Geltsdale nature reserve, Cumbria], we surveyed waders over four visits in fields where rush was managed according to AES prescriptions (treatment; n = 21) and fields without rush management that were otherwise ecologically similar (control; n = 22) to assess how the densities of breeding wader pairs respond to rush management in the short-term. We find evidence for regional variation in the response of waders to rush management, with densities of Common Snipe Gallinago gallinago significantly higher in treatment than control fields in the SWP, but not Geltsdale. There were no statistically significant responses to treatment on densities of Eurasian Curlew Numenius arquata or Northern Lapwing Vanellus vanellus. The 95% confidence intervals for the treatment parameter estimates suggest that this may be due to limited statistical power in the case of Lapwing. For Curlew, however, any potential increases in densities are negligible. There was no evidence that variation in rush cover, which ranged from 10 to 70%, influenced densities of any of our three focal species. Our results suggest that rush management through AES prescriptions delivered in isolation of other interventions may not lead to general increases in breeding wader densities in the short-term, but benefits may arise in some situations due to regional and inter-specific variation in effectiveness. Rush management supported with interventions that improve soil conditions and thus food availability, or reduce predation pressure, may enable AES rush management to generate benefits. Additional research is required to maximise the potential benefits of rush management for each species through the development of prescriptions that tailor to individual species' optimum sward structure.</p>
Figure 6 from: Albratty M, Thangavel N, Chandrasekaran B, Meraya AM, Alhazmi HA, Muthumanickam S, Boomi P, Bhagavan NB, Saleh SF (2024) Benchmarking docking, density functional theory and molecular dynamics studies to assess the aldose reductase inhibitory potential of Trigonella foenum-graecum compounds for managing diabetes-associated complications. Pharmacia 71: 1-10. https://doi.org/10.3897/pharmacia.71.e118949
Figure 6 Molecular dynamics of Tigogenin and Gitogenin bound to aldose reductase: (a) RMSD, (b) RMSF, (c) Hydrogen bond profile; green-Tigogenin, red-Gitogenin, black-apoprotein.
Supplementary material 1 from: Albratty M, Thangavel N, Chandrasekaran B, Meraya AM, Alhazmi HA, Muthumanickam S, Boomi P, Bhagavan NB, Saleh SF (2024) Benchmarking docking, density functional theory and molecular dynamics studies to assess the aldose reductase inhibitory potential of Trigonella foenum-graecum compounds for managing diabetes-associated complications. Pharmacia 71: 1-10. https://doi.org/10.3897/pharmacia.71.e118949
List of molecular weight matched decoys retrieved from DEKOIS 2.0
Figure 5 from: Albratty M, Thangavel N, Chandrasekaran B, Meraya AM, Alhazmi HA, Muthumanickam S, Boomi P, Bhagavan NB, Saleh SF (2024) Benchmarking docking, density functional theory and molecular dynamics studies to assess the aldose reductase inhibitory potential of Trigonella foenum-graecum compounds for managing diabetes-associated complications. Pharmacia 71: 1-10. https://doi.org/10.3897/pharmacia.71.e118949
Figure 5 HOMO and LUMO distribution plots: (a) HOMO (b) LUMO of Tigogenin (c) HOMO (d) LUMO of Gitogenin.
Figure 4 from: Albratty M, Thangavel N, Chandrasekaran B, Meraya AM, Alhazmi HA, Muthumanickam S, Boomi P, Bhagavan NB, Saleh SF (2024) Benchmarking docking, density functional theory and molecular dynamics studies to assess the aldose reductase inhibitory potential of Trigonella foenum-graecum compounds for managing diabetes-associated complications. Pharmacia 71: 1-10. https://doi.org/10.3897/pharmacia.71.e118949
Figure 4 Intermolecular interactions of (a) Tigogenin, (b) Gitogenin, (c) Epalrestat with active-site amino acids of the enzyme aldose reductase. All the ligands are shown in all atoms' green-coloured ball and stick-type representations. The names of amino acids with ID numbers are mentioned under each circle around each ligand. On the 2D figures analysis, green-coloured dotted lines indicate hydrogen bonding interactions involving electronegative elements like nitrogen and oxygen atoms; light purple-coloured dotted lines indicate π-alkyl interactions; violet-coloured dotted lines indicate π-sigma interactions. Light green colour amino acids without bonding represent van der Waals interactions, whereas, orange-red colour amino acids indicate unfavourable interactions. The light-blue halo surrounding the interacting residues represents the solvent-accessible surface that is proportional to its diameter.
Figure 1 from: Albratty M, Thangavel N, Chandrasekaran B, Meraya AM, Alhazmi HA, Muthumanickam S, Boomi P, Bhagavan NB, Saleh SF (2024) Benchmarking docking, density functional theory and molecular dynamics studies to assess the aldose reductase inhibitory potential of Trigonella foenum-graecum compounds for managing diabetes-associated complications. Pharmacia 71: 1-10. https://doi.org/10.3897/pharmacia.71.e118949
Figure 1 The binding site of aldose reductase was predicted using CASTp. The residues highlighted in blue boxes constitute the binding site.
Figure 3 from: Albratty M, Thangavel N, Chandrasekaran B, Meraya AM, Alhazmi HA, Muthumanickam S, Boomi P, Bhagavan NB, Saleh SF (2024) Benchmarking docking, density functional theory and molecular dynamics studies to assess the aldose reductase inhibitory potential of Trigonella foenum-graecum compounds for managing diabetes-associated complications. Pharmacia 71: 1-10. https://doi.org/10.3897/pharmacia.71.e118949
Figure 3 Analysis and comparison of the predictive power of AutoDock and AutoDock Vina: (a) Receiver operating characteristic curves, (b) Predictiveness curves, (c) Enrichment curves, red is ADock and green is Avina.
Exploration of the electron density and bare nuclear potential along the selected normal modes of the selected water clusters - W6 - changedHexamers
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Exploration of the electron density and bare nuclear potential along the selected normal modes of the selected water clusters - W6
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Density functional theory study of Mobius boroncarbon-nitride as potential CH4, H2S, NH3, COCl2 and CH3OH gas sensor
<p>The interesting properties of Mobius structure and Boron-Carbon-Nitride inspired this research to study different characteristics of Mobius Boron-Carbon-Nitride (MBCN) nanoribbon. The structural stability, vibrational, electrical, and optical properties are analyzed using the density functional theory. The gas sensing ability of the modeled MBCN structure was also studied for CH<sub>4</sub>, H<sub>2</sub>S, NH<sub>3</sub>, COCl<sub>2</sub>, and CH<sub>3</sub>OH gases. The negative adsorption energy and alteration of electronic bandgap verified that MBCN is very sensitive toward the selected gases. The complex structures showed a high absorption coefficient with strong chemical potential and 7 ps- 0.3 ms recovery time. The negative change in entropy signifies that all the complex structures were thermodynamically stable. Among the selected gases, the MBCN showed the strongest interaction with CH<sub>3</sub>OH gas.</p>
Data from: Palaeoecological implications of the preservation potential of soft-bodied organisms in sediment-density flows: testing turbulent waters
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Annotated Behaviour and Observability Dataset (ABODe)
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