This paper focuses on the activation of boro-alumino-silicate glass from discarded pharmaceutical containers as fine powders suspended in relatively diluted alkaline solutions (2.5 M NaOH/KOH), followed by drying at 40°C for 7 days. A specific molecular mechanism of glass hardening is proposed. Unlike geopolymers, the hardening does not result from the condensation of products of extensive dissolution. Instead, condensation reactions occur in hydrated surface layers, leading to strong bonds (Si-O-Si, Al-O-Si, etc.) between individual glass particles. Silicates, borates, and aluminates from glass dissolution combine with alkaline ions, yielding additional soluble phases. This mechanism is supported by the results of solid-state NMR spectroscopy and elemental analysis using ICP-OES. Stable matrices prepared by direct foaming or by the inclusion of cenospheres or commercial expanded glass (Poraver®) can be exploited for the fabrication of lightweight components

High throughput toxicokinetic (HTTK) methods address chemical risk assessment data gaps but require chemical-specific values that can be obtained by in vitro measurements or in silico models. In this study, seven quantitative structure property relationship (QSPR) models were used to estimate intrinsic hepatic clearance (Clint), fraction of chemical unbound in plasma (fup), and/or TK elimination half-life (t½). Performance of the QSPR models was evaluated using literature time-course in vivo TK data, mainly from rats. Simulations of the in vivo data were made with a high throughput physiologically based TK (HT-PBTK) model using the different QSPR model predictions as inputs. We estimate that using rat in vivo data to evaluate QSPR models trained on human in vitro measured data might inflate error estimates by as much as root mean squared log10 error (RMSLE) 0.8. A sensitivity analysis showed that Clint and fup parameters inform predictions of area under the curve (AUC) and steady-state concentration (Css). We estimate that AUC can be predicted by HTTK with RMSLE 0.9 using in vitro measurements and 0.6-0.8 using QSPR model values. We anticipate that, for some novel compounds, QSPRs for HTTK input parameters will give predictions of TK similar to those based on in vitro measurements.

Graphitic carbon nitride (gC3N4, or CN herein) is widely studied as a photocatalyst owing to its ease of synthesis, high stability, and optoelectronic properties. However, its photocatalytic performance often remains limited, and a common approach to tune its function and enhance its performance is by doping. Boron (B) functionalization of CN has showed a potential benefit on photocatalytic performance for several reactions. However, the reason for this improvement and the links between synthesis method, exact B chemical environment, and performance remain unclear. Here, we present a fundamental study that elucidates the influence of (i) B functionalization, (ii) B content, and (iii) choice of B precursor on the physicochemical, adsorptive, optoelectronic, and photocatalytic properties of bulk B-CN. We synthesized two sets of B-CN materials (0.5-11 at% B), using either elemental boron or boric acid as precursors. The samples were characterized using several imaging and spectroscopic techniques, which confirm the integration of B into the material through B-O bonding and the creation of B clusters in the case of the boron precursor, with density functional theory (DFT) calculations supporting our analyses. The distribution of B atoms within B-CN particles remained heterogeneous. Compared to CN, B-functionalized materials show enhanced porosity and CO2 uptake, with similar degrees of light absorption and deeper energy band positions. Transient absorption spectroscopy (TAS) measurements showed that charge carrier populations, lifetimes, and kinetics were not significantly affected by B functionalization; however, at 5 at% B doping, an increase in the concentration of charge carriers was seen. Higher B content enhances the photocatalytic NO x removal under UVA irradiation (almost two-fold) and the selectivity to NO3 - from NO x photooxidation, but has no significant effect on CO2 photoreduction, compared to pristine CN. Overall, this study provides fundamental insights to build on and more rationally produce better-performing B-CN photocatalysts.

The combination of multinuclear NMR spectroscopy with 17O isotopic enrichment and DFT calculations provided detailed insight into both the bulk and surface structure of γ-Al2O3. Comparison of experimental 17O NMR spectra to computational predictions confirmed that bulk γ-Al2O3 contains Al cations primarily in "spinel-like" sites, with roughly equal numbers of alternating AlVI and AlIV vacancies in disordered "chains". The work showed that overlap of signals from OIV and OIII species complicates detailed spectral analysis and highlighted potential problems with previous work where structural conclusions are based on an unambiguous assignment (and quantification) of these signals. There was no evidence for the presence of H, or for any significant levels of O vacancies, in the bulk structure of γ-Al2O3. Computational predictions from structural models for different surfaces showed a wide variety of protonated and non-protonated O species occur. Assignment of signals for two types of protonated O species was achieved using variable temperature CP and TRAPDOR experiments, with the sharper and broader resonances attributed to more accessible surface sites that interact more strongly with water and less accessible aluminols, respectively. DFT-predicted 1H NMR parameters confirmed the 1H shift increases with denticity but is also dependent on the coordination number of the next nearest neighbour Al species. Spectral assignments were also supported by 1H-27Al RESPDOR experiments, which identified spectral components resulting from μ1, μ2 and μ3 aluminols. Combining these with 1H-27Al D-HMQC experiments showed that (i) μ1 aluminols are more likely to be bound to AlIV, (ii) μ2 aluminols are coordinated to all three types of Al, but with a higher proportion bound to similar types of Al and (iii) μ3 aluminols are most likely bound to higher coordinated Al species. 1H DQ MAS spectroscopy confirmed no aluminols exist exclusively in isolation but showed that the closest proximities are between bridging aluminols coordinated to AlIV and/or AlV species.

Microporous AlPO 4 STA-1 has been synthesised and studied by NMR crystallography; it is an effective catalyst for the Beckmann rearrangement of cyclohexanone oxime.

We report here a method of making polyketones from the coupling of diketones and diols using a manganese pincer complex. The methodology allows us to access various polyketones (polyarylalkylketone) containing aryl, alkyl, and ether functionalities, bridging the gap between the two classes of commercially available polyketones: aliphatic polyketones and polyaryletherketones. Using this methodology, 12 polyketones have been synthesized and characterized using various analytical techniques to understand their chemical, physical, morphological, and mechanical properties. Based on previous reports and our studies, we suggest that the polymerization occurs via a hydrogen-borrowing mechanism that involves the dehydrogenation of diols to dialdehyde followed by aldol condensation of dialdehyde with diketones to form chalcone derivatives and their subsequent hydrogenation to form polyarylalkylketones.

We characterise the solid form of the lichen metabolite, usnic acid, using NMR crystallography to achieve absolute assignment of all observed signals. 13 C NMR spectroscopy shows that usnic acid in wild Usnea lichen has the same crystalline form.

We report the first synthesis of the mixed-metal chabazite-type AlxGa1-xPO4-34(mim) solid solution, containing 1-methylimidazolium, mim, as structure directing agent (SDA), from the parent mixed-metal oxide solid solution, gamma-(AlxGa1-x)2O(3). This hitherto unreported family of materials exhibits complex disorder, arising from the possible distributions of cations over available sites, the orientation of the SDA and the presence of variable amounts of water, which provides a prototype for understanding structural subtleties in nanoporous materials. In the as-made forms of the phosphate frameworks, there are three crystallographically distinct metal sites: two tetrahedral MO4 and one octahedral MO4F2 (M = Al, Ga). A combination of solid-state NMR spectroscopy and periodic DFT calculations reveals that the octahedral site is preferentially occupied by Al and the tetrahedral sites by Ga, leading to a non-random distribution of cations within the framework. Upon calcination to the AlxGa1-xPO4-34 framework, all metal sites are tetrahedral and crystallographically equivalent in the average R3 symmetry. The cation distribution was explored by P-31 solid-state NMR spectroscopy, and it is shown that the non-random distribution demonstrated to exist in the as-made materials would be expected to give remarkably similar patterns of peak intensities to a random distribution owing to the change in average symmetry in the calcined materials.

Continuous flow synthesis of hypercrosslinked polymers (HCPs) was achieved within 5 minutes, 99% faster than a batch reaction, improving productivity rates by 32-fold. The faster kinetics also enhanced microporosity, increasing CO2/N2selectivity by 850%.

Per- and polyfluoroalkyl substances (PFAS) are a diverse group of man-made chemicals that are commonly found in body tissues. The toxicokinetics of most PFAS are currently uncharacterized, but long half-lives (t½) have been observed in some cases. Knowledge of chemical-specific t½ is necessary for exposure reconstruction and extrapolation from toxicological studies. We used an ensemble machine learning method, random forest, to model the existing in vivo measured t½ across four species (human, monkey, rat, mouse) and eleven PFAS. Mechanistically motivated descriptors were examined, including two types of surrogates for renal transporters: (1) physiological descriptors, including kidney geometry, for renal transporter expression and (2) structural similarity of defluorinated PFAS to endogenous chemicals for transporter affinity. We developed a classification model for t½ (Bin 1: <12 h; Bin 2: <1 week; Bin 3: <2 months; Bin 4: >2 months). The model had an accuracy of 86.1% in contrast to 32.2% for a y-randomized null model. A total of 3890 compounds were within domain of the model, and t½ was predicted using the bin medians: 4.9 h, 2.2 days, 33 days, and 3.3 years. For human t½, 56% of PFAS were classified in Bin 4, 7% were classified in Bin 3, and 37% were classified in Bin 2. This model synthesizes the limited available data to allow tentative extrapolation and prioritization.

‘Cell Painting’ is an imaging-based high-throughput phenotypic profiling (HTPP) method in which cultured cells are fluorescently labeled to visualize subcellular structures (i.e., nucleus, nucleoli, endoplasmic reticulum, cytoskeleton, Golgi apparatus / plasma membrane and mitochondria) and to quantify morphological changes in response to chemicals or other perturbagens. HTPP is a high-throughput and cost-effective bioactivity screening method that detects effects associated with many different molecular mechanisms in an untargeted manner, enabling rapid in vitro hazard assessment for thousands of chemicals. Here, 1201 chemicals from the ToxCast library were screened in concentration-response up to ∼100 μM in human U-2 OS cells using HTPP. A phenotype altering concentration (PAC) was estimated for chemicals active in the tested range. PACs tended to be higher than lower bound potency values estimated from a broad collection of targeted high-throughput assays, but lower than the threshold for cytotoxicity. In vitro to in vivo extrapolation (IVIVE) was used to estimate administered equivalent doses (AEDs) based on PACs for comparison to human exposure predictions. AEDs for 18/412 chemicals overlapped with predicted human exposures. Phenotypic profile information was also leveraged to identify putative mechanisms of action and group chemicals. Of 58 known nuclear receptor modulators, only glucocorticoids and retinoids produced characteristic profiles; and both receptor types are expressed in U-2 OS cells. Thirteen chemicals with profile similarity to glucocorticoids were tested in a secondary screen and one chemical, pyrene, was confirmed by an orthogonal gene expression assay as a novel putative GR modulating chemical. Most active chemicals demonstrated profiles not associated with a known mechanism-of-action. However, many structurally related chemicals produced similar profiles, with exceptions such as diniconazole, whose profile differed from other active conazoles. Overall, the present study demonstrates how HTPP can be applied in screening-level chemical assessments through a series of examples and brief case studies.

Cocoa pod husks (CPHs) represent an underutilized component of the chocolate manufacturing process. While industry's current focus is understandably on the cocoa beans, the husks make up around 75 wt % of the fruit. Previous studies have been dominated by the carbohydrate polymers present in CPHs, but this work highlights the presence of the biopolymer lignin in this biomass. An optimized organosolv lignin isolation protocol was developed, delivering significant practical improvements. This new protocol may also prove to be useful for agricultural waste-derived biomasses in general. NMR analysis of the high quality lignin led to an improved structural understanding, with evidence provided to support deacetylation of the lignin occurring during the optimized pretreatment. Chemical transformation, using a tosylation, azidation, copper-catalyzed click protocol, delivered a modified lignin oligomer with an organophosphorus motif attached. Thermogravimetric analysis was used to demonstrate the oligomer's potential as a flame-retardant. Preliminary analysis of the other product streams isolated from the CPHs was also carried out.

Toxicokinetic (TK) models have been used for decades to estimate concentrations of per-and polyfluoroalkyl substances (PFAS) in serum. However, model complexity has varied across studies depending on the application and the state of the science. This scoping effort seeks to systematically map the current landscape of PFAS TK models by categorizing different trends and similarities across model type, PFAS, and use scenario. A literature review using Web of Science and SWIFT-Review was used to identify TK models used for PFAS. The assessment covered publications from 2005–2020. PFOA, the PFAS for which most models were designed, was included in 69 of the 92 papers, followed by PFOS with 60, PFHxS with 22, and PFNA with 15. Only 4 of the 92 papers did not include analysis of PFOA, PFOS, PFNA, or PFHxS. Within the corpus, 50 papers contained a one-compartment model, 17 two-compartment models were found, and 33 used physiologically based pharmacokinetic (PBTK) models. The scoping assessment suggests that scientific interest has centered around two chemicals—PFOA and PFOS—and most analyses use one-compartment models in human exposure scenarios.

An efficient protocol for the calculation of 13C pNMR shifts in metal–organic frameworks based on Cu(ii) paddlewheel dimers is proposed, which involves simplified structural models, optimised using GFN2-xTB for the high-spin state, and CAM-B3LYP-computed NMR and EPR parameters. Models for hydrated and activated HKUST-1 and hydrated STAM MOFs with one, two and three Cu dimers have been used. The electronic ground states are low-spin and diamagnetic, with pNMR shifts arising from thermal population of intermediate- and high-spin excited states. Treating individual spin configurations in a broken symmetry (BS) approach, and selecting two or more of these to describe individual excited states, the magnetic shieldings of these paramagnetic states are evaluated using the approach by Hrobárik and Kaupp. The total shielding is then evaluated from a Boltzmann distribution between the energy levels of the chosen configurations. The computed pNMR shifts are very sensitive to temperature and, therefore, to the relative energies of the BS spin states. In order to reproduce the temperature dependence of the pNMR shifts seen in experiment, some scaling of the calculated energy gaps is required. A single scaling factor was applied to all levels in any one system, by fitting to experimental results at several temperatures simultaneously. The resulting scaling factor decreases with an increasing number of dimer units in the model (e.g., from ∼1.7 for mono-dimer models to 1.2 for tri-dimer models). The approach of this scaling factor towards unity indicates that models with three dimers are approaching a size where they can be considered as reasonable models for the 13C shifts of infinite MOFs. The observed unusual temperature dependencies in the latter are indicated to arise both from the “normal” temperature dependence of the pNMR shifts of the paramagnetic states and the populations of these states in the thermal equilibrium.

The US Environmental Protection Agency (USEPA) employs a tiered process for assessing risks of pesticides to bees. The model discussed in this paper focuses on honey bees (Apis mellifera L.). If risks to honey bees are identified at the first tier based on exposure and toxicity data for individual adult and larval honey bees, then effects are evaluated in higher-tier studies using honey bee colonies. Colony-level studies require large amounts of resources (to conduct and review) and can yield data complicated by the inherent variability of colonies, which are influenced by factors that cannot readily be controlled, including weather, pests, diseases, available forage, and bee management practices. To better interpret these data, the USEPA and the US Department of Agriculture (USDA) developed a simulation model, BeePop+, that assesses potential honey bee colony-level effects of pesticides. Here, we describe this model using the population model guidance, use, interpretation, and development for ecological risk assessment (Pop-GUIDE) framework, which is a conceptual framework for the development and evaluation of population models. Within the context of Pop-GUIDE, BeePop+ is considered a “realistic-precise” model and reflects the inherent variability of colony response to pesticide exposure by simulating many outcomes. This model meets the desired features needed for use in pesticide risk assessments as its required data inputs are typically available, it is applicable to different US locations, and the outputs are both relevant to USEPA’s protection goals for honey bees and are consistent with the outcomes of empirical studies. This model has also been evaluated using available empirical colony-level data; however, additional evaluation with other studies may still be done in the future prior to completing implementation.

1,4-Dioxane is a persistent and mobile organic chemical that has been found by the United States Environmental Protection Agency (USEPA) to be an unreasonable risk to human health in some occupational contexts. 1,4-Dioxane is released into the environment as industrial waste and occurs in some personal-care products as an unintended byproduct. However, limited exposure assessments have been conducted outside of an occupational context. In this study, the USEPA simulation modeling tool, Stochastic Human Exposure and Dose Simulator-High Throughput (SHEDS-HT), was adapted to estimate the exposure and chemical mass released down the drain (DTD) from drinking water consumption and product use. 1,4-Dioxane concentrations measured in drinking water and consumer products were used by SHEDS-HT to evaluate and compare the contributions of these sources to exposure and mass released DTD. Modeling results showed that compared to people whose daily per capita exposure came from only products (2.29 × 10-7 to 2.92 × 10-7 mg/kg/day), people exposed to both contaminated water and product use had higher per capita median exposures (1.90 × 10-6 to 4.27 × 10-6 mg/kg/day), with exposure mass primarily attributable to water consumption (75-91%). Last, we demonstrate through simulation that while a potential regulatory action could broadly reduce DTD release, the proportional reduction in exposure would be most significant for people with no or low water contamination.

FeAPO-34 with a chabazite (CHA) topology structure was successfully synthesized under ionothermal conditions using 1-ethyl-3-methylimidazoliumchloride (EMIMCl) ionic liquid in the presence of ethylenediamine (EDA). The material was characterized using powder X-ray diffraction (XRD), thermogravimetric analysis (TGA), elemental analyses, and solid-state NMR spectroscopy. Incorporation of iron within the covalent framework of the material was confirmed by the presence of broad signals between 1000 and 14,000 ppm in the 31P NMR spectrum, corresponding to the P(OFe)x(OAl)4-x species.

Exposure to chemicals is influenced by associations between the individual’s location and activities as well as demographic and physiological characteristics. Currently, many exposure models simulate individuals by drawing distributions from population-level data or use exposure factors for single individuals. The Residential Population Generator (RPGen) binds US surveys of individuals and households and combines the population with physiological characteristics to create a synthetic population. In general, the model must be supported by internal consistency; i.e., values that could have come from a single individual. In addition, intraindividual variation must be representative of the variation present in the modeled population. This is performed by linking individuals and similar households across income, location, family type, and house type. Physiological data are generated by linking census data to National Health and Nutrition Examination Survey data with a model of interindividual variation of parameters used in toxicokinetic modeling. The final modeled population data parameters include characteristics of the individual’s community (region, state, urban or rural), residence (size of property, size of home, number of rooms), demographics (age, ethnicity, income, gender), and physiology (body weight, skin surface area, breathing rate, cardiac output, blood volume, and volumes for body compartments and organs). RPGen output is used to support user-developed chemical exposure models that estimate intraindividual exposure in a desired population. By creating profiles and characteristics that determine exposure, synthetic populations produced by RPGen increases the ability of modelers to identify subgroups potentially vulnerable to chemical exposures. To demonstrate application, RPGen is used to estimate exposure to Toluene in an exposure modeling case example.

The intrinsic metabolic clearance rate (Cl-int) and fraction of chemical unbound in plasma (f(up)) serve as important parameters for high throughput toxicokinetic models, but experimental data are limited for many chemicals. Open-source quantitative structure-activity relationship (QSAR) models for both parameters were developed to offer reliable in silico predictions for a diverse set of chemicals regulated under U.S. law, including pharmaceuticals, pesticides, and industrial chemicals. As a case study to demonstrate their utility, model predictions served as inputs to the TK component of a risk-based prioritization approach based on Bioactivity: Exposure Ratios (BER), in which a BER < 1 indicates exposures are predicted to exceed a biological activity threshold. When applied to a subset of the Tox21 screening library (6631 chemicals) we found that the proportion of chemicals with BER < 1 was similar using either in silico (1337/6631; 20.16%) or in vitro (151/850; 17.76%) parameters. Further, when considering only the chemicals in the Tox21 set with in vitro data, there was a high concordance of chemicals classified with either BER < 1 or >1 using either in silico or in vitro parameters (776/850, 91.30%). Thus, the presented QSARs may be suitable for prioritizing the risk posed by many chemicals for which measured in vitro TK data are lacking.