Suite Of Models Research Articles

Lightning-Fast Convective Outlooks: Predicting Severe Convective Environments With Global AI-Based Weather Models.

Severe convective storms are among the most dangerous weather phenomena and accurate forecasts mitigate their impacts. The recently released suite of AI-based weather models produces medium-range forecasts within seconds, with a skill similar to state-of-the-art operational forecasts for variables on single levels. However, predicting severe thunderstorm environments requires accurate combinations of dynamic and thermodynamic variables and the vertical structure of the atmosphere. Advancing the assessment of AI-models toward process-based evaluations lays the foundation for hazard-driven applications. We assess the forecast skill of the top-performing AI-models GraphCast, Pangu-Weather and FourCastNet for convective parameters at lead-times up to 10days against reanalysis and ECMWF's operational numerical weather prediction model IFS. In a case study and seasonal analyses, we see the best performance by GraphCast and Pangu-Weather: these models match or even exceed the performance of IFS for instability and shear. This opens opportunities for fast and inexpensive predictions of severe weather environments.

A methodology for deciding on well seal options for abandonment

The energy transition to achieve net zero anthropogenic CO 2 emissions will require permanently sealing and abandoning wells or boreholes drilled for many different purposes, including: underground CO 2 storage; hydrogen storage; geothermal energy extraction; site characterization for radioactive waste repositories; and hydrocarbon extraction. It is necessary to objectively decide what sealing materials, combination of materials and methods of emplacement are optimal for a given well sealing project, taking into account the properties of lithologies penetrated, the physical and geochemical conditions around the well, the geometry of the well, and characteristics of well engineering. A workflow that would allow for a structured approach to designing well seals during well decommissioning and a set of complementary tools that will enable an informed, structured, transparent and traceable process for designing a well decommissioning sealing solution for a particular well in a given geological environment is presented in this paper. The tools include: (1) a database of Features Events and Processes; (2) a decision tree for evaluating different well sealing options and for documenting the rationale for decisions; (3) an approach for comparing options using multiple decision trees; (4) and a suite of simplified numerical models to inform judgements.

Enhancing Supply Chain Efficiency with Predictive Analytics: A Machine Learning Approach to Dynamic Lead Time Prediction in a Chemical Industry

We develop and employ a four-step methodological approach for predicting the lead time delays in all echelons of a supply chain (SC). The first step of the methodological approach involves a critical synthesis of academic research efforts for identifying the main sources of delays in all echelons of a supply chain. The second step involves the development of questionnaires for validating the findings of the research through workshops with industry stakeholders. The third step involves the development of a suite of machine learning (ML) models, namely, Random Forest Regression, Decision Tree Regression, and Linear Regression. These models were selected based on their prevalence in the recent literature and their ability to handle linear and nonlinear relationships between multiple variables. The final fourth step involves the implementation of the suite of machine learning models in the real case of a Hellenic chemical manufacturing supply chain. The implementation results reveal that Random Forest Regression exhibits the highest predictive accuracy throughout all stages of the supply chain, achieving the lowest Mean Absolute Percentage Errors (MAPE), ranging from 0.5 to 7% in the examined supply chain echelons.

IMMU-36. MULTI-OMIC CHARACTERIZATION OF A SYNGENEIC MOUSE CANCER STEM CELL MODEL OF GBM FOR PRECLINICAL APPLICATIONS

Abstract Glioblastoma (GBM) is resistant to many therapies including immunotherapies. There is an urgent need to explore the role of the microenvironment in resistance to therapy with tailored pre-clinical models. Using multi-omic approaches we describe a syngeneic cancer stem cell mouse model of GBM, with a spontaneous amplification of Igf2. We investigate whether Igf2 influences tumour and microenvironment cells to promote immunosuppression in GBM. Three cell lines were previously established from spontaneous brain tumours in C57Bl/6 Trp53+/-/Nf1+/- mice and maintained under neural stem cell culture conditions. Whole genome sequencing (WGS) was used to determine single nucleotide, copy number, and structural variation, revealing loss of both copies of Trp53 and Nf1 in all three cell lines. However only one cell line, mBT0309, developed tumors when orthotopically allografted. Analysis of genomic alterations and transcriptomics revealed that mBT309 exhibits amplification of the Igf2 loci and overexpression was confirmed at the RNA and protein level. Spatial and single cell transcriptomics showed that Igf2 is overexpressed in mBT0309 allografted tumours and correlated with specific transcriptomic programs. A high-parameter Imaging Mass Cytometry (IMCTM) panel was used for spatial proteomic analysis, to monitor the development of tumours in a time-course experiment. In vitro growth characteristics and stem marker expression in both transcriptomic and spatial IMC data suggest that high levels of Igf2 may regulate GBM stemness features. Analysis of human GBM datasets revealed that Igf2 amplified tumours have reduced CXCL11 expression. These findings suggest that high levels of Igf2 may influence immunosuppression by reducing T-cell recruitment. This syngeneic immunocompetent GBM stem cell model harbouring Igf2 amplification adds to the suite of models to study the GBM microenvironment and its role in immunosuppression and resistance to therapy.

The Use of Image‐Based Data and Abundance Modelling Approaches for Predicting the Location of Vulnerable Marine Ecosystems in the South Pacific Ocean

ABSTRACTVulnerable marine ecosystems (VMEs) are typically fragile and slow to recover, thereby making them susceptible to disturbance, including fishing. In the high seas, the United Nations General Assembly (UNGA) requested regional fishery management organisations (RFMOs) to implement measures to prevent significant adverse impacts on VMEs. Here, we predict spatial abundances of 15 taxa, 13 VME indicator taxa, in the South Pacific RFMO (SPRFMO) area. Models used seafloor imagery data, an important advance on previously developed presence‐only predictions, to provide information on spatial variation in taxa abundance that is crucial for better inferring likely location of VMEs, rather than just distribution of VME indicator taxa. Abundance models varied in predictive power (mean R2 ranged 0.02–0.40). Uncertainty estimates of model predictions were developed to inform future spatial planning processes for conservation and management of VMEs. Using the VME index concept, abundance model outputs and previously published presence‐only model predictions were weighted using vulnerability scores, to explore how modelled outputs could provide spatial estimates of likely VME distribution. Spatial predictions of abundance improved on previous modelling to provide an almost complete suite of abundance models for VME indicator taxa in the western portion of the SPRFMO Convention area. Nevertheless, to improve utility of modelled outputs, we recommend more high‐quality seafloor imagery data be gathered within the SPRFMO Convention area to (1) validate abundance models developed here with independent data from the model area, (2) update models, if necessary, (3) link abundance information to ecosystem function and (4) explore validity of the adapted VME index approach used here.

“Halfway to Rayleigh” and Other Insights into the Rossby Wave Instability

The Rossby wave instability (RWI) is the fundamental nonaxisymmetric radial shear instability in disks. The RWI can facilitate disk accretion, set the shape of planetary gaps, and produce large vortices. It arises from density and/or temperature features, such as radial gaps, bumps, or steps. A general, sufficient condition to trigger the RWI is lacking, which we address by studying the linear RWI in a suite of simplified models, including incompressible and compressible shearing sheets and global, cylindrical disks. We focus on enthalpy amplitude and width as the fundamental properties of disk features with various shapes. We find analytic results for the RWI boundary and growth rates across a wide parameter space, in some cases with exact derivations and in others as a description of numerical results. Features wider than a scale height generally become unstable about halfway to Rayleigh instability, i.e., when the squared epicyclic frequency is about half the Keplerian value, reinforcing our previous finding. RWI growth rates approximately scale as enthalpy amplitude to the 1/3 power, with a weak dependence on width, across much of the parameter space. Global disk curvature affects wide planetary gaps, making the outer gap edge more susceptible to the RWI. Our simplified models are barotropic and height integrated, but the main results should carry over to more complex and realistic scenarios.

IFLOW: A Framework and GUI to Quantify Effective Thermal Diffusivity and Advection in Permeable Materials From Temperature Time Series

AbstractiFLOW is a free, open‐source, and python‐based framework and graphical user interface to visualize and analyze temperature time series, and extract one dimensional thermal velocity, vT, and bulk effective thermal diffusivity, ke. Information of thermal properties of the sediment‐water mixture (bulk) and water allows quantifying the one‐dimensional Darcian flux, q, and seepage velocity, v, from vT. Available software packages were developed to quantify q and ke only based on a specific mathematical model or focused on specific data processing or parameter estimation techniques, and all these steps were lumped together preventing users to identify potential source of errors. iFLOW proposes a novel organizational philosophy with a modular framework that parses the analysis process into three fundamental steps: (a) the mathematical model, (b) signal processing, and (c) parameter estimation. iFLOW houses a suite of models and analysis techniques. This suite can be readily added to and expanded through its modular framework. iFLOW contains a wizard to guide users through the selection process with respect to the three fundamental steps. Users can analyze and visualize intermediate results to identify problematic issues in the time series data and improve data interpretation. Here, we present iFLOW and summarize its performance using a set of one‐dimensional synthetic heat transport simulations.

The Large-scale Anisotropy and Flux (de)magnification of Ultrahigh-energy Cosmic Rays in the Galactic Magnetic Field

We calculate the arrival direction distribution of ultrahigh-energy cosmic rays (UHECRs) with a new suite of models of the Galactic magnetic field (GMF), assuming sources follow the large-scale structure of the Universe. Compared to previous GMF models, the amplitude of the dipole component of the UHECR arrival flux is significantly reduced. We find that the reduction is due to the accidentally coinciding position of the peak of the extragalactic UHECR flux and the boundary of strong flux demagnification due to the GMF toward the central region of the Galaxy. This serendipitous sensitivity of UHECR anisotropies to the GMF model will be a powerful probe of the source distribution as well as Galactic and extragalactic magnetic fields. Demagnification by the GMF also impacts the visibility of some popular source candidates.

Bidimensional Exploration of the warm-Temperature Ionised gaS (BETIS)

The extraplanar diffuse ionised gas (eDIG) is a key component for understanding the feedback processes that connect galactic discs and their halos. In this paper, we present the second study of the Bidimensional Exploration of the warm-Temperature Ionised Gas (BETIS) project, the aim of which is to explore the possible ionisation mechanisms and characteristics of the eDIG. We use a sample of eight edge-on galaxies observed with the Multi-Unit Spectroscopic Explorer (MUSE) integral field spectrograph (IFS) and apply the methodology developed in the first paper of the BETIS project for obtaining binned emission line maps. We find that the vertical and radial profiles of the [N II]/Hα, [S II]/Hα, [O III]/Hβ, and [O I]/Hα ratios depict a complex ionisation structure within galactic halos – which is influenced by the spatial distribution of H II regions across the galactic plane as observed from our line of sight–, with Lyman continuum photon leakage from OB associations constituting the main ionisation source. Moreover, the electron temperature and S+/S ionisation ratio also exhibit a dependency on the distribution of H II regions within the galactic discs. Our analysis excludes low-mass, hot, and evolved stars (HOLMES) as viable candidates for secondary ionisation sources to elucidate the unusual behaviour of the line ratios at greater distances from the galactic midplane. In contrast, we ascertain that shocks induced in the interstellar medium by star formation(SF)-related feedback mechanisms represent a promising secondary ionisation source of the eDIG. We present a suite of models integrating ionisation mechanisms arising from fast shocks and photoionisation associated with star formation. When applied to the classical Baldwin–Phillips–Terlevich (BPT) diagrams, these models reveal that the ionisation budget of the eDIG ranges from 20% to 50% across our sample, with local variations of up to 20% within individual galaxy halos. This contribution correlates with the presence of filaments and other structural components observed within galaxy halos. The presence of shocks is additionally supported by the observation of a high density of high [O I]/Hα ratios, which is characteristic of shock-compressed ionised gas, and is likely induced by feedback from regions of intense SF within the galactic disc. These results demonstrate consistency across all galaxies analysed in this sample.

Shape-dependent aerosol dynamics in indoor environments: Penetration, deposition, and dispersion

Particle shape exerts a significant influence on their dynamic behavior, and it is imperative to elucidate these effects given the potential for severe environmental toxicity associated with shaped particles. Despite extensive research on the dynamical processes of spherical particles, the behaviors of non-spherical particles have been insufficiently investigated. In this study, we have developed a suite of computation-based models that account for particle shape and have reported on the typical dynamical behaviors of non-spherical particles within indoor environments. We have explored three typical scenarios, i.e., particle penetration into indoor spaces through building cracks, indoor particle deposition, and indoor particle dispersion. The shape-induced deviations are associated with dynamical processes, showing a decrease trend among penetration, deposition, and dispersion of the non-spherical particles. The maximum discrepancy due to particle shape during the penetration process exceeds 1000 %, observed with particles of approximately 0.02 μm in diameter interacting with straight cracks 4.5 cm in length and 0.25 mm in height. Moreover, there is a discrepancy of more than 70 % in the deposition of particles with a diameter of approximately 10 μm on side walls when using side air supply ventilation. Similarly, a discrepancy of nearly 11 % is noted for particles around 0.02 μm in diameter during dispersion under displacement ventilation within indoor settings. The interaction between shape-related particle dynamics, particularly their diffusion characteristics, and the properties of the flow field leads to these shape-dependent dynamical discrepancies. These findings offer a comprehensive understanding of how the shape of particles affects their indoor dynamic behavior, thereby supporting the control of hazardous particles in indoor environments.

Biogenic silica dynamics in coastal wetland sediments: A key driver of silicon and carbon biogeochemical cycling

AbstractBiogenic silica (biogenic Si) is a bioactive component crucial for the biogeochemical cycling of Si in terrestrial and aquatic ecosystems. Its formation and dissolution dynamics are intricately linked to carbon (C) cycling. However, knowledge about the source, composition, and factors controlling the distribution of biogenic Si in coastal wetland sedimentary environments is still limited. To address this lack of knowledge, we introduced a suite of geometric models for biogenic Si biovolume calculation and investigated biogenic Si assemblages, along with biogenic Si and biogenic Si‐occluded C contents, in sediments from representative coastal wetlands along the west coast of Bohai Bay. Our analysis showed that sedimentary biogenic Si predominantly derived from phytoliths (78.1% ± 5.8%), diatoms (18.2% ± 4.8%), and sponge spicules (3.7% ± 2.9%). Notably, phytolith assemblages were primarily composed of forms derived from the Poaceae family. The biogenic Si‐occluded C content (0.035–1.870 g kg−1) within these wetland sediments was consistent across both sites, accounting for 0.97–5.71% of the total organic C pool. Structural equation modeling indicated that total organic C, pH, and amorphous aluminum oxides either directly or indirectly influenced the biogenic Si content in coastal wetlands sediments. These results demonstrate the critical role that biogenic Si plays in the Si biogeochemical cycle and provide valuable information that advances our understanding of the biogeochemical interactions between Si and C in coastal wetland ecosystems.

Deceptively simple: An outsider's perspective on natural language processing

AbstractThis article highlights a collection of ideas with an underlying deceptive simplicity that addresses several practical challenges in computational social science and generative AI safety. These ideas lead to (1) an interpretable and quantifiable framework for political polarization; (2) a language identifier robust to noisy social media text settings; (3) a cross‐lingual semantic sampler that harnesses code‐switching; and (4) a bias audit framework that uncovers shocking racism, antisemitism, misogyny, and other biases in a wide suite of large language models.

The added value of using convective-permitting regional climate model simulations to represent cloud band events over South America

Climate science has long explored whether higher resolution regional climate models (RCMs) provide improved simulation of regional climates over global climate models (GCMs). The advent of convective-permitting RCMs (CPRCMs), where sufficiently fine-scale grids allow explicitly resolving rather than parametrising convection, has created a clear distinction between RCM and GCM formulations. This study investigates the simulation of tropical-extratropical (TE) cloud bands in a suite of pan-South America convective-permitting Met Office Unified Model (UM) and Weather Research and Forecasting (WRF) climate simulations. All simulations produce annual cycles in TE cloud band frequency within 10–30% of observed climatology. However, too few cloud band days are simulated during the early summer (Nov–Dec) and too many during the core summer (Jan–Feb). Compared with their parent forcing, CPRCMs simulate more dry days but systematically higher daily rainfall rates, keeping the total rain biases low. During cloud band systems, the CPRCMs correctly reproduced the observed changes in tropical rain rates and their importance to climatology. Circulation analysis suggests that simulated lower subtropical rain rates during cloud bands systems, in contrast to the higher rates in the tropics, are associated with weaker northwesterly moisture flux from the Amazon towards southeast South America, more evident in the CPRCMs. Taken together, the results suggest that CPRCMs tend to be more effective at producing heavy daily rainfall rates than parametrised simulations for a given level of near-surface moist energy. The extent to which this improves or degrades biases present in the parent simulations is strongly region-dependent.

Summer roost site selection of a declining bat species

AbstractSummer habitats are critical to bat population persistence as they support multiple life history stages, including maternity colonies, nursery sites, and foraging locations. The tricolored bat (Perimyotis subflavus) is a hibernating North American bat species that uses forested landscapes during summer months; however, information on the summer habitat requirements is limited. The objective of this work was to quantify the characteristics of roost sites selected by tricolored bats during summer months. We captured, tagged, and tracked 15 bats using radio-telemetry to 55 roost locations. At each roost, we recorded roost habitat characteristics and other forest characteristics within a 0.1 ha circular plot surrounding the roost tree and a 1 km buffer at the landscape scale. We repeated these measurements for three random trees per roost tree to characterize available habitat for selection. We used a suite of mixed conditional logistic regression models to test multiple factors known to influence roost-site selection for various bat species and compared using Akaike information criterion to select the best model. The top model at the roost scale demonstrated that roost selection was influenced by roost tree height, while the landscape scale was influenced by deciduous forest and distance to roads. There is a critical information gap for the ongoing recovery of tricolored bats; better understanding of summer habitat and proper forest management implications, as well as information on scale-specific habitat selection, is needed to better understand tricolored bat management needs.

A Suite of Foundation Models Captures the Contextual Interplay Between Codons.

In the canonical genetic code, many amino acids are assigned more than one codon. Work by us and others has shown that the choice of these synonymous codon is not random, and carries regulatory and functional consequences. Existing protein foundation models ignore this context-dependent role of coding sequence in shaping the protein landscape of the cell. To address this gap, we introduce cdsFM, a suite of codon-resolution large language models, including both EnCodon and DeCodon models, with up to 1B parameters. Pre-trained on 60 million protein-coding sequences from more than 5,000 species, our models effectively learn the relationship between codons and amino acids, recapitualing the overall structure of the genetic code. In addition to outperforming state-of-the-art genomic foundation models in a variety of zero-shot and few-shot learning tasks, the larger pre-trained models were superior in predicting the choice of synonymous codons. To systematically assess the impact of synonymous codon choices on protein expression and our models' ability to capture these effects, we generated a large dataset measuring overall and surface expression levels of three proteins as a function of changes in their synonymous codons. We showed that our EnCodon models could be readily fine-tuned to predict the contextual consequences of synonymous codon choices. Armed with this knowledge, we applied EnCodon to existing clinical datasets of synonymous variants, and we identified a large number of synonymous codons that are likely pathogenic, several of which we experimentally confirmed in a cell-based model. Together, our findings establish the cdsFM suite as a powerful tool for decoding the complex functional grammar underlying the choice of synonymous codons.

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.

The Impact of Drought on Terrestrial Carbon in the West African Sahel: Implications for Natural Climate Solutions

AbstractTerrestrial ecosystems store more than twice the carbon of the atmosphere, and are critical to climate change mitigation efforts. This has led to a proliferation of land‐based carbon sequestration efforts, such as re/afforestation associated with the Great Green Wall in the West African Sahel (WAS GGW). However, we currently lack comprehensive assessments of the long‐term viability of these ecosystems' carbon storage in the context of increasingly severe climate extremes. The WAS is particularly prone to recurrent and disruptive extremes, exemplified by the persistent and severe late‐20th century drought. We assessed the response and recovery of WAS GGW carbon stocks and fluxes to this late‐20th century drought, and the subsequent rainfall recovery, by leveraging a suite of terrestrial ecosystem models. While multi‐model mean carbon fluxes (e.g., gross primary production, respiration) partly recovered to pre‐drought levels, modeled total (above and below ground) ecosystem carbon stock falls to as much as two standard deviations below pre‐drought levels and does not recover even ∼20 years after the maximum drought anomaly. Furthermore, to the extent that the modeled regional carbon stock recovers, it is nearly entirely driven by atmospheric CO2 trends rather than the precipitation recovery. Uncertainties in regional ecosystem carbon simulation are high, as the models' carbon responses to drought displayed a nearly 10‐standard deviation spread. Nevertheless, the multi‐model average response highlights the strong and persistent impact of drought on terrestrial carbon storage, and the potential risks of relying on terrestrial ecosystems as a “natural climate solution” for climate change mitigation.

First measurement of the Mg ii forest correlation function in the Epoch of Reionization

ABSTRACT Strong low-ionization transitions like the Mg ii$\lambda 2796,2804$ Å doublet are believed to produce a detectable ‘metal-line forest’, if metals pollute the neutral intergalatic medium (IGM). We measure the autocorrelation of the Mg ii forest transmission using 10 ground-based $z \ge 6.80$ quasar spectra probing the redshift range $5.96 \lt z_{\rm Mg\, {\small II}} \lt 7.42$ ($z_{\rm Mg\, {\small II}, median} = 6.47$). The correlation function exhibits strong small-scale clustering and a pronounced peak at the doublet velocity ($\Delta v = 768~{\rm km\, s^{-1}}$) arising from discrete absorbers in the circumgalactic medium of galaxies. After these strong absorbers are identified and masked the signal is consistent with noise. Our measurements are compared to a suite of models generated by combining a large hydrodynamical simulation with a seminumerical reionization topology, assuming a simple uniform enrichment model. We obtain a 95 per cent credibility upper limit of $[{\rm Mg/H}] \lt -3.73$ at $z_{\rm Mg\, {\small II},median} = 6.47$, assuming uninformative priors on [Mg/H] and the IGM neutral fraction $x_{\rm {H\, {\small I}}}$. Splitting the data into low-z ($5.96 \lt z_{\rm Mg\, {\small II}} \lt 6.47$; $z_{\rm Mg\, {\small II},median} = 6.235$) and high-z ($6.47 \lt z_{\rm Mg\, {\small II}} \lt 7.42$; $z_{\rm Mg\, {\small II},median} = 6.72$) subsamples again yields null detections and 95 per cent upper limits of $[{\rm Mg/H}] \lt -3.75$ and $\lt -3.45$, respectively. These first measurements set the stage for making the Mg ii forest an emerging tool to precisely constrain the Universe reionization and enrichment history.

Exploring the influences of salinity on CO2 plume migration and storage capacity in sloping formations: A numerical investigation

Accurate evaluation of CO2 plume migration, potential leakage, and storage capacity are the main challenges for CO2 geological storage (CGS). This study develops a suite of numerical models, focusing on the Shiqianfeng Formation of the Shenhua-Carbon Capture and Storage (CCS) demonstration project in the Ordos Basin, China, aiming to numerically evaluate the influence of combining salinity, dip angle, and faulting factors on CO2 plume migration, storage amount, conversion of gas and dissolved phase states. Simulation results show that lower salinity and larger dip angles result in earlier CO2 leakage. Increasing salinity from 0.00 to S in a 10° sloping formation causes later CO2 leakage (205–260 years). Lower salinity enhances CO2 plume migration, injection amount, and dissolved-phase CO2. As the salinity decreased from S to 0.00 in a 3° sloping formation over 120 years of CO2 migration, the dissolved CO2 plume migration increased by 9.1 %, the total CO2 amount increased by 70.0 %, and the proportion of dissolved CO2 increased by 13.07 %. Compared to formation dip angle, salinity has a more significant impact on the plume migration of dissolved CO2. However, CO2 storage safety decreases with increasing dip angle. The conversion proportion (1.88 %–9.75 %) of gas-phase to dissolved CO2 increased with an increasing dip angle and decreasing salinity for the first 100 years after injection ceased. Thus, we conclude that salinity, dip angle and faulting are important factors during CGS, and this study may provide a practical reference for further studies for evaluating CO2 storage potential.

Theoretical insight on electronic and optical properties of some phosphorescent iridium(III) complexes for organic light-emitting diode devices

Organic light-emitting diode (OLED) structures have been extensively investigated because of their potential applications in various fields. It is known that organic or metal-mediated organic compounds are used in the layers of OLEDs. Within OLED materials, iridium(III) compounds have attract much attention owing to their many advantages. Therefore, we predicted the OLED behaviors of twenty-five phosphorescent iridium(III) complexes using theoretical chemical methods. All theoretical calculations were performed with the B3LYP hybrid functional using Gaussian 16 and Amsterdam Modeling Suite 2023 programs. While the 6–31G(d) basis set for non-metal atoms and LANL2DZ basis set for iridium metal was preferred in the computations carried out by using Gaussian program, whereas in the calculations performed with the Amsterdam Modelling Suite program, the TZP basis set was used for all atoms. From the theoretically obtained results, it is seen that Ir6, Ir7, Ir22-Ir25 complexes can be proposed as good candidate for hole injection layer materials in OLED based on indium tin oxide as an anode. Within complexes investigated, it has been determined that Ir16 and Ir17 complexes are the most suitable candidates for electron transport layer materials, whereas Ir16 complex can be used as both a hole transfer layer and ambipolar materials. Furthermore, it has been predicted that Ir4, Ir7, Ir8, and Ir23 complexes could be preferred as hole blocking layer compounds. From the detailed analysis of the singlet-triplet transitions of the complexes studied, it could be said that all iridium(III) complexes investigated could be used as highly efficient phosphorescent OLED molecules.