Probing the self-ionization of liquid water with ab initio deep potential molecular dynamics.

The chemical equilibrium between self-ionized and molecular water dictates the acid-base chemistry in aqueous solutions, yet understanding the microscopic mechanisms of water self-ionization remains experimentally and computationally challenging. Herein, Density Functional Theory (DFT)-based deep neural network (DNN) potentials are combined with enhanced sampling techniques and a global acid-base collective variable to perform extensive atomistic simulations of water self-ionization for model systems of increasing size. The explicit inclusion of long-range electrostatic interactions in the DNN potential is found to be crucial to accurately reproduce the DFT free energy profile of solvated water ion pairs in small (64 and 128 H2O) cells. The reversible work to separate the hydroxide and hydronium to a distance [Formula: see text] is found to converge for simulation cells containing more than 500 H2O, and a distance of [Formula: see text] 8 Å is the threshold beyond which the work to further separate the two ions becomes approximately zero. The slow convergence of the potential of mean force with system size is related to a restructuring of water and an increase of the local order around the water ions. Calculation of the dissociation equilibrium constant illustrates the key role of long-range electrostatics and entropic effects in the water autoionization process.

Water dissociation at the water-rutile TiO2(110) interface from ab initio-based deep neural network simulations.

The interaction of water with TiO2 surfaces is of crucial importance in various scientific fields and applications, from photocatalysis for hydrogen production and the photooxidation of organic pollutants to self-cleaning surfaces and bio-medical devices. In particular, the equilibrium fraction of water dissociation at the TiO2-water interface has a critical role in the surface chemistry of TiO2, but is difficult to determine both experimentally and computationally. Among TiO2 surfaces, rutile TiO2(110) is of special interest as the most abundant surface of TiO2's stable rutile phase. While surface-science studies have provided detailed information on the interaction of rutile TiO2(110) with gas-phase water, much less is known about the TiO2(110)-water interface, which is more relevant to many applications. In this work, we characterize the structure of the aqueous TiO2(110) interface using nanosecond timescale molecular dynamics simulations with ab initio-based deep neural network potentials that accurately describe water/TiO2(110) interactions over a wide range of water coverages. Simulations on TiO2(110) slab models of increasing thickness provide insight into the dynamic equilibrium between molecular and dissociated adsorbed water at the interface and allow us to obtain a reliable estimate of the equilibrium fraction of water dissociation. We find a dissociation fraction of 22 ± 6% with an associated average hydroxyl lifetime of 7.6 ± 1.8 ns. These quantities are both much larger than corresponding estimates for the aqueous anatase TiO2(101) interface, consistent with the higher water photooxidation activity that is observed for rutile relative to anatase.

Very large fluxes of methane measured above Bolivian seasonal wetlands.

Methane (CH4) mole fractions from the large semiseasonal Llanos de Moxos wetlands (∼70,000 km2) in northern Bolivia were measured by aircraft flights and ground sampling during early March 2019 (late wet season). Daily fluxes of CH4 determined from the measurements using box models and inverse modeling were between 168 (± 50) and 456 (± 145) mg CH4⋅m-2⋅d-1 for the areas overflown, very high compared with those of previous Amazon basin studies. If the seasonality of the CH4 emissions is comparable to other parts of the Amazon Basin, the region could contribute as much as 8% of annual Amazonian CH4 emissions.

Oxidative potential of particulate matter and its association to respiratory health endpoints in high-altitude cities in Bolivia.

Exposure to particulate matter (PM) pollution is a significant health risk, driving the search for innovative metrics that more accurately reflect the potential harm to human health. Among these, oxidative potential (OP) has emerged as a promising health-based metric, yet its application and relevance across different environments remain to be further explored. This study, set in two high-altitude Bolivian cities, aims to identify the most significant sources of PM-induced oxidation in the lungs and assess the utility of OP in assessing PM health impacts. Utilizing two distinct assays, OPDTT and OPDCFH, we measured the OP of PM samples, while also examining the associations between PM mass, OP, and black carbon (BC) concentrations with hospital visits for acute respiratory infections (ARI) and pneumonia over a range of exposure lags (0-2 weeks) using a Poisson regression model adjusted for meteorological conditions. The analysis also leveraged Positive Matrix Factorization (PMF) to link these health outcomes to specific PM sources, building on a prior source apportionment study utilizing the same dataset. Our findings highlight anthropogenic combustion, particularly from traffic and biomass burning, as the primary contributors to OP in these urban sites. Significant correlations were observed between both OPDTT and PM2.5 concentration exposure and ARI hospital visits, alongside a notable association with pneumonia cases and OPDTT levels. Furthermore, PMF analysis demonstrated a clear link between traffic-related pollution and increased hospital admissions for respiratory issues, affirming the health impact of these sources. These results underscore the potential of OPDTT as a valuable metric for assessing the health risks associated with acute PM exposure, showcasing its broader application in environmental health studies.

Leptodelphax maculigera (Hemiptera: Delphacidae) harbors the corn stunt complex pathogens.

The African planthopper Leptodelphax maculigera (Hemiptera: Delphacidae) has been recently reported in many places in Brazil in association with maize. Its occurrence in maize production fields in Brazil has brought concerns to the corn production chain regarding the possibility of this planthopper to be a vector for maize bushy stunt phytoplasma (MBSP), corn stunt spiroplasma (Spiroplasma kunkelii), maize rayado fino virus (MRFV) and maize striate mosaic virus (MSMV). The phytoplasma and spiroplasma, that are bacteria belonging to the Class Mollicutes, and the two viruses are associated with the corn stunt disease complex. Given the presence of the African planthopper species and the corn stunt disease complex in Brazil, we further investigated the abundance of this planthopper species in Santa Catarina state, Brazil, and whether the planthopper can carry the four pathogens. We inspected 12 maize production fields in different municipalities in the state for 20 weeks, using two yellow sticky traps for each maize field. The sticky traps were replaced weekly. A total of 130 specimens of L. maculigera were captured, with a great discrepancy in quantity among locations and weeks. We detected the mollicute MBSP, and the viruses MRFV and MSMV in L. maculigera, whereas S. kunkelii was absent in the assessed African planthopper samples. The molecular detection of the phytoplasma and the viruses in field-collected African planthoppers is a strong evidence that this insect species has the ability of acquiring those pathogens through feeding from phloem of diseased maize plants. Nonetheless, transmission capacity needs to be experimentally proven to assert L. maculigera as a vector for the corn stunting pathogens.

Proteomic Analysis of a Rat Streptozotocin Model Shows Dysregulated Biological Pathways Implicated in Alzheimer's Disease.

Alzheimer's Disease (AD) is an age-related neurodegenerative disorder characterized by progressive memory loss and cognitive impairment, affecting 35 million individuals worldwide. Intracerebroventricular (ICV) injection of low to moderate doses of streptozotocin (STZ) in adult male Wistar rats can reproduce classical physiopathological hallmarks of AD. This biological model is known as ICV-STZ. Most studies are focused on the description of behavioral and morphological aspects of the ICV-STZ model. However, knowledge regarding the molecular aspects of the ICV-STZ model is still incipient. Therefore, this work is a first attempt to provide a wide proteome description of the ICV-STZ model based on mass spectrometry (MS). To achieve that, samples from the pre-frontal cortex (PFC) and hippocampus (HPC) of the ICV-STZ model and control (wild-type) were used. Differential protein abundance, pathway, and network analysis were performed based on the protein identification and quantification of the samples. Our analysis revealed dysregulated biological pathways implicated in the early stages of late-onset Alzheimer's disease (LOAD), based on differentially abundant proteins (DAPs). Some of these DAPs had their mRNA expression further investigated through qRT-PCR. Our results shed light on the AD onset and demonstrate the ICV-STZ as a valid model for LOAD proteome description.

Elucidating the water-anatase TiO2(101) interface structure using infrared signatures and molecular dynamics.

The structure and dynamics of water on solid surfaces critically affect the chemistry of materials in ambient and aqueous environments. Here, we investigate the hydrogen bonding network of water adsorbed on the majority (101) surface of anatase TiO2, a widely used photocatalyst, using polarization- and azimuth-resolved infrared spectroscopy combined with neural network potential molecular dynamics simulations. Our results show that one monolayer of water saturates the undercoordinated titanium (Ti5c) sites, forming one-dimensional chains of molecule hydrogen bonded to surface undercoordinated bridging oxygen (O2c) atoms. As the coverage increases, water adsorption on O2c sites leads to significant restructuring of the water monolayer and the formation of a two-dimensional hydrogen bond network characterized by tightly bound pairs of water molecules on adjacent Ti5c and O2c sites. This structural motif likely persists at ambient conditions, influencing the reactions occurring there. The results reported here provide critical details of the structure of the water-anatase (101) interface that were previously hypothesized but unconfirmed experimentally.

δ13C methane source signatures from tropical wetland and rice field emissions.

The atmospheric methane (CH4) burden is rising sharply, but the causes are still not well understood. One factor of uncertainty is the importance of tropical CH4 emissions into the global mix. Isotopic signatures of major sources remain poorly constrained, despite their usefulness in constraining the global methane budget. Here, a collection of new δ13CCH4 signatures is presented for a range of tropical wetlands and rice fields determined from air samples collected during campaigns from 2016 to 2020. Long-term monitoring of δ13CCH4 in ambient air has been conducted at the Chacaltaya observatory, Bolivia and Southern Botswana. Both long-term records are dominated by biogenic CH4 sources, with isotopic signatures expected from wetland sources. From the longer-term Bolivian record, a seasonal isotopic shift is observed corresponding to wetland extent suggesting that there is input of relatively isotopically light CH4 to the atmosphere during periods of reduced wetland extent. This new data expands the geographical extent and range of measurements of tropical wetland and rice δ13CCH4 sources and hints at significant seasonal variation in tropical wetland δ13CCH4 signatures which may be important to capture in future global and regional models. This article is part of a discussion meeting issue 'Rising methane: is warming feeding warming? (part 2)'.

Isotopic signatures of methane emissions from tropical fires, agriculture and wetlands: the MOYA and ZWAMPS flights.

We report methane isotopologue data from aircraft and ground measurements in Africa and South America. Aircraft campaigns sampled strong methane fluxes over tropical papyrus wetlands in the Nile, Congo and Zambezi basins, herbaceous wetlands in Bolivian southern Amazonia, and over fires in African woodland, cropland and savannah grassland. Measured methane δ13CCH4 isotopic signatures were in the range -55 to -49‰ for emissions from equatorial Nile wetlands and agricultural areas, but widely -60 ± 1‰ from Upper Congo and Zambezi wetlands. Very similar δ13CCH4 signatures were measured over the Amazonian wetlands of NE Bolivia (around -59‰) and the overall δ13CCH4 signature from outer tropical wetlands in the southern Upper Congo and Upper Amazon drainage plotted together was -59 ± 2‰. These results were more negative than expected. For African cattle, δ13CCH4 values were around -60 to -50‰. Isotopic ratios in methane emitted by tropical fires depended on the C3 : C4 ratio of the biomass fuel. In smoke from tropical C3 dry forest fires in Senegal, δ13CCH4 values were around -28‰. By contrast, African C4 tropical grass fire δ13CCH4 values were -16 to -12‰. Methane from urban landfills in Zambia and Zimbabwe, which have frequent waste fires, had δ13CCH4 around -37 to -36‰. These new isotopic values help improve isotopic constraints on global methane budget models because atmospheric δ13CCH4 values predicted by global atmospheric models are highly sensitive to the δ13CCH4 isotopic signatures applied to tropical wetland emissions. Field and aircraft campaigns also observed widespread regional smoke pollution over Africa, in both the wet and dry seasons, and large urban pollution plumes. The work highlights the need to understand tropical greenhouse gas emissions in order to meet the goals of the UNFCCC Paris Agreement, and to help reduce air pollution over wide regions of Africa. This article is part of a discussion meeting issue 'Rising methane: is warming feeding warming? (part 2)'.

Detection and Quantification of Enteric Pathogens in Aerosols Near Open Wastewater Canals in Cities with Poor Sanitation.

Urban sanitation infrastructure is inadequate in many low-income countries, leading to the presence of highly concentrated, uncontained fecal waste streams in densely populated areas. Combined with mechanisms of aerosolization, airborne transport of enteric microbes and their genetic material is possible in such settings but remains poorly characterized. We detected and quantified enteric pathogen-associated gene targets in aerosol samples near open wastewater canals (OWCs) or impacted (receiving sewage or wastewater) surface waters and control sites in La Paz, Bolivia; Kanpur, India; and Atlanta, USA, via multiplex reverse-transcription qPCR (37 targets) and ddPCR (13 targets). We detected a wide range of enteric targets, some not previously reported in extramural urban aerosols, with more frequent detections of all enteric targets at higher densities in La Paz and Kanpur near OWCs. We report density estimates ranging up to 4.7 × 102 gc per mair3 across all targets including heat-stable enterotoxigenic Escherichia coli, Campylobacter jejuni, enteroinvasive E. coli/Shigella spp., Salmonella spp., norovirus, and Cryptosporidium spp. Estimated 25, 76, and 0% of samples containing positive pathogen detects were accompanied by culturable E. coli in La Paz, Kanpur, and Atlanta, respectively, suggesting potential for viability of enteric microbes at the point of sampling. Airborne transmission of enteric pathogens merits further investigation in cities with poor sanitation.

Antimicrobial resistance genes are enriched in aerosols near impacted urban surface waters in La Paz, Bolivia.

Antibiotic resistance poses a major global health threat. Understanding emergence and dissemination of antibiotic resistance in environmental media is critical to the design of control strategies. Because antibiotic resistance genes (ARGs) may be aerosolized from contaminated point sources and disseminated more widely in localized environments, we assessed ARGs in aerosols in urban La Paz, Bolivia, where wastewater flows in engineered surface water channels through the densely populated urban core. We quantified key ARGs and a mobile integron (MI) via ddPCR and E. coli spp. as a fecal indicator by culture over two years during both the rainy and dry seasons in sites near wastewater flows. ARG targets represented major antibiotic groups-tetracyclines (tetA), fluoroquinolines (qnrB), and beta-lactams (blaTEM)-and an MI (intI1) represented the potential for mobility of genetic material. Most air samples (82%) had detectable targets above the experimentally determined LOD: most commonly blaTEM and intI1 (68% and 47% respectively) followed by tetA and qnrB (17% and 11% respectively). ARG and MI densities in positive air samples ranged from 1.3 × 101 to 6.6 × 104 gene copies/m3 air. Additionally, we detected culturable E. coli in the air (52% of samples <1 km from impacted surface waters) with an average density of 11 CFU/m3 in positive samples. We observed decreasing density of blaTEM with increasing distance up to 150 m from impacted surface waters. To our knowledge this is the first study conducting absolute quantification and a spatial analysis of ARGs and MIs in ambient urban air of a city with contaminated surface waters. Environments in close proximity to urban wastewater flows in this setting may experience locally elevated concentrations of ARGs, a possible concern for the emergence and dissemination of antimicrobial resistance in cities with poor sanitation.

When do short-range atomistic machine-learning models fall short?

We explore the role of long-range interactions in atomistic machine-learning models by analyzing the effects on fitting accuracy, isolated cluster properties, and bulk thermodynamic properties. Such models have become increasingly popular in molecular simulations given their ability to learn highly complex and multi-dimensional interactions within a local environment; however, many of them fundamentally lack a description of explicit long-range interactions. In order to provide a well-defined benchmark system with precisely known pairwise interactions, we chose as the reference model a flexible version of the Extended Simple Point Charge (SPC/E) water model. Our analysis shows that while local representations are sufficient for predictions of the condensed liquid phase, the short-range nature of machine-learning models falls short in representing cluster and vapor phase properties. These findings provide an improved understanding of the role of long-range interactions in machine learning models and the regimes where they are necessary.

Raman spectrum and polarizability of liquid water from deep neural networks.

We introduce a scheme based on machine learning and deep neural networks to model the environmental dependence of the electronic polarizability in insulating materials. Application to liquid water shows that training the network with a relatively small number of molecular configurations is sufficient to predict the polarizability of arbitrary liquid configurations in close agreement with ab initio density functional theory calculations. In combination with a neural network representation of the interatomic potential energy surface, the scheme allows us to calculate the Raman spectra along 2-nanosecond classical trajectories at different temperatures for H2O and D2O. The vast gains in efficiency provided by the machine learning approach enable longer trajectories and larger system sizes relative to ab initio methods, reducing the statistical error and improving the resolution of the low-frequency Raman spectra. Decomposing the spectra into intramolecular and intermolecular contributions elucidates the mechanisms behind the temperature dependence of the low-frequency and stretch modes.

Ab initio theory and modeling of water.

Water is of the utmost importance for life and technology. However, a genuinely predictive ab initio model of water has eluded scientists. We demonstrate that a fully ab initio approach, relying on the strongly constrained and appropriately normed (SCAN) density functional, provides such a description of water. SCAN accurately describes the balance among covalent bonds, hydrogen bonds, and van der Waals interactions that dictates the structure and dynamics of liquid water. Notably, SCAN captures the density difference between water and ice Ih at ambient conditions, as well as many important structural, electronic, and dynamic properties of liquid water. These successful predictions of the versatile SCAN functional open the gates to study complex processes in aqueous phase chemistry and the interactions of water with other materials in an efficient, accurate, and predictive, ab initio manner.

Ion dehydration controls adsorption at the micellar interface: hydrotropic ions.

The properties of ionic micelles depend on the nature of the counterion, and these effects become more evident as the ion adsorption at the interface increases. Prediction of the relative extent of ion adsorption is required for rational design of ionic micellar aggregates. Unlike the well understood adsorption of monatomic ions, the adsorption of polyatomic ions is not easily predicted. We combined experimental and computational methods to evaluate the affinity of hydrotropic ions, i.e., ions with polar and apolar regions, to the surface of positively charged micelles. We analyzed cationic micelles of dodecyltrimethylammonium and six hydrotropic counterions: methanesulfonate, trifluoromethanesulfonate, benzenesulfonate, acetate, trifluoroacetate and benzoate. Our results demonstrated that the apolar region of hydrotropic ions had the largest influence on micellar properties. The dehydration of the apolar region of hydrotropic ions upon their adsorption at the micellar interface determined the ion adsorption extension, differently to what was expected based on Collins' law of matching affinities. These results may lead to more general models to describe the adsorption of ions, including polyatomic ions, at the micellar interface.

Nonlinear Effects of Hydrophobic Confinement on the Electronic Structure and Dielectric Response of Water.

Fundamental studies of the dielectrics of confined water are critical to understand the ion transport across biological and synthetic nanochannels. The relevance of these fundamental studies, however, surmounts the difficulty of probing water's dielectric constant as a function of a fine variation in confinement. In this work, we explore the computational efficiency of machine learning potentials to derive the confinement effects on the dielectric constant, polarization, and dipole moment of water. Our simulations predict an enhancement of the axial dielectric constant of water under extreme confinement, arising from either the formation of ferroelectric structures of ordered water or larger dipole fluctuations facilitated by the disruption of water's H-bond network. Our study highlights the impact of hydrophobic nanoconfinement on the dielectric constant and on the ionic and electronic structure of water molecules, pointing to the importance of geometric flexibility and electronic polarizability to properly model confinement effects on water.

Latitudinal and temporal distribution of aerosols and precipitable water vapor in the tropical Andes from AERONET, sounding, and MERRA-2 data.

The aerosol and precipitable water vapor (PW) distribution over the tropical Andes region is characterized using Aerosol Robotic Network (AERONET) observations at stations in Medellin (Colombia), Quito (Ecuador), Huancayo (Peru), and La Paz (Bolivia). AERONET aerosol optical depth (AOD) is interpreted using PM2.5 data when available. Columnar water vapor derived from ozone soundings at Quito is used to compare against AERONET PW. MERRA-2 data are used to complement analyses. Urban pollution and biomass burning smoke (BBS) dominate the regional aerosol composition. AOD and PM2.5 yearly cycles for coincident measurements correlate linearly at Medellin and Quito. The Andes cordillera's orientation and elevation funnel or block BBS transport into valleys or highlands during the two fire seasons that systematically impact South America. The February-March season north of Colombia and the Colombian-Venezuelan border directly impacts Medellin. Possibly, the March aerosol signal over Quito has a long-range transport component. At Huancayo and La Paz, AOD increases in September due to the influence of BBS in the Amazon. AERONET PW and sounding data correlate linearly but a dry bias with respect to soundings was identified in AERONET. PW and rainfall progressively decrease from north to south due to increasing altitude. This regional diagnosis is an underlying basis to evaluate future changes in aerosol and PW given prevailing conditions of rapidly changing atmospheric composition.

Confinement Effects on Proton Transfer in TiO2 Nanopores from Machine Learning Potential Molecular Dynamics Simulations.

Improved understanding of proton transfer in nanopores is critical for a wide range of emerging applications, yet experimentally probing mechanisms and energetics of this process remains a significant challenge. To help reveal details of this process, we developed and applied a machine learning potential derived from first-principles calculations to examine water reactivity and proton transfer in TiO2 slit-pores. We find that confinement of water within pores smaller than 0.5 nm imposes strong and complex effects on water reactivity and proton transfer. Although the proton transfer mechanism is similar to that at a TiO2 interface with bulk water, confinement reduces the activation energy of this process, leading to more frequent proton transfer events. This enhanced proton transfer stems from the contraction of oxygen-oxygen distances dictated by the interplay between confinement and hydrophilic interactions. Our simulations also highlight the importance of the surface topology, where faster proton transport is found in the direction where a unique arrangement of surface oxygens enables the formation of an ordered water chain. In a broader context, our study demonstrates that proton transfer in hydrophilic nanopores can be enhanced by controlling pore size, surface chemistry, and topology.

Nuclear Quantum Effects on the Electronic Structure of Water and Ice.

The electronic properties and optical response of ice and water are intricately shaped by their molecular structure, including the quantum mechanical nature of the hydrogen atoms. Despite numerous previous studies, a comprehensive understanding of the nuclear quantum effects (NQEs) on the electronic structure of water and ice at finite temperatures remains elusive. Here, we utilize molecular simulations that harness efficient machine-learning potentials and many-body perturbation theory to assess how NQEs impact the electronic bands of water and hexagonal ice. By comparing path-integral and classical simulations, we find that NQEs lead to a larger renormalization of the fundamental gap of ice, compared to that of water, ultimately yielding similar bandgaps in the two systems, consistent with experimental estimates. Our calculations suggest that the increased quantum mechanical delocalization of protons in ice, relative to water, is a key factor leading to the enhancement of NQEs on the electronic structure of ice.

Castor (Ricinus communis L.) differential cell cycle and metabolism reactivation, germinability, and seedling performance under NaCl and PEG osmoticum: Stress tolerance related to genotype-preestablished superoxide dismutase activity.

Castor (Ricinus communis) is a relevant industrial oilseed feedstock for many industrial applications, being globally mainly cultivated by smallholder farmers in semiarid areas, where abiotic stresses predominate. Therefore, susceptible to generating reactive oxygen species (ROS) and subsequent oxidative stress, compromising cell metabolism upon seed imbibition and germination, seedling and crop establishment, and yield. The present study evaluated the consequences of water restriction by Polyethylene glycol (PEG) and Sodium chloride (NaCl) on cell cycle and metabolism reactivation on germinability, seedling growth, and vigor parameters in 2 commercial castor genotypes (Nordestina and Paraguaçu). PEG water restriction inhibited germination completely at -0.23 MPa or higher, presumably due to reduced oxygen availability. The restrictive effects of NaCl saline stress on germination were observed only from -0.46 MPa onwards, affecting dry mass accumulation and the production of normal seedlings. In general, superoxide dismutase (SOD) activity increased in NaCl -0.23 MPa, whereas its modulation during the onset of imbibition (24h) seemed to depend on its initial levels in dry seeds in a genotype-specific manner, therefore, resulting in the higher stress tolerance of Nordestina compared to Paraguaçu. Overall, results show that Castor germination and seedling development are more sensitive to the restrictive effects of PEG than NaCl at similar osmotic potentials, contributing to a better understanding of the responses to water restriction stresses by different Castor genotypes. Ultimately, SOD may constitute a potential marker for characterizing castor genotypes in stressful situations during germination, early seedling, and crop establishment, and a target for breeding for Castor-improved stress tolerance.