Advancing life cycle based chemical toxicity characterization through digitalization

Chemicals are vital to modern society, but their rapid production and use lead to hazardous emissions, affecting human and ecosystem health. Chemical toxicity characterization is an essential tool to help assess and mitigate these impacts but requires diverse chemical input data that are unavailable for most of the >350,000 globally registered chemicals and mixtures. Machine learning (ML) methods have achieved remarkable predictive performance across scientific fields and offer high potential to fill these data gaps across input parameters and chemicals. However, the systematic uptake of ML methods to address data gaps in chemical toxicity characterization remains limited due to challenges undermining confidence in their predictions. In particular, ML’s limited extrapolative capacity constrains reliable predictions to domains represented within the training data. This obscures which data gaps across various chemicals and input parameters can be effectively addressed by developing ML prediction methods. Further, integrating predictions from different ML models in chemical assessments requires quantifying uncertainty in predictions to account for input data quality variations. However, uncertainty quantification is challenging and not standard in ML practice. Therefore, practical examples of developing and integrating ML-based predictions with quantified uncertainty into chemical toxicity characterization are urgently needed to build trust in prediction-based chemical assessments.The work presented in this PhD thesis addresses these challenges by focusing on four research objectives: (1) Prioritize input parameters in characterizing human toxicity and ecotoxicity impacts for developing ML-based approaches based on their relevance for obtaining robust characterization results and their suitability for ML. (2) Analyze the chemical space covered by ML-based approaches trained with available measured data for relevant input parameters for characterizing human toxicity and ecotoxicity impacts relative to the global chemical market. (3) Identify and develop suitable ML-based approaches capable of quantifying data- and model-related uncertainty in predictions across diverse chemical structures. (4) Demonstrate the use of uncertain ML-based predictions to gain insights on chemical toxicity for the global chemical market and for designing safer and more sustainable chemical synthesis using illustrative case studies.Following an introductory chapter, chapter 2 presents a framework for prioritizing input data gaps in chemical toxicity characterization, prioritizing 13 out of its 38 input parameters for ML model development while flagging additional nine parameters with critical data gaps. Chapter 3 offers an assessment of the potential for ML to address the broader realm of >130,000 marketed chemicals for the prioritized parameters, finding that based on 1 to 10% of available data, ML can potentially predict 8 to 46% of marketed chemicals. This predictive potential was highly dependent on the chemical diversity represented in the available input parameter data. These results demonstrated that ML can significantly contribute to filling data gaps in chemical toxicity characterization. However, it left several crucial input parameters and more than 50% of marketed chemicals across prioritized parameters difficult to address. Chapter 2 and 3 highlighted that strategic efforts are needed to increase data availability focusing on data diversity and advanced modeling approaches to leverage alternative data sources and domain knowledge. Chapter 4 presents an approach for developing uncertainty-aware ML models that transparently communicate prediction reliability through fully quantified uncertainty intervals. It demonstrates that both conformal prediction (CP) and Bayesian neural networks (BNN) can provide robust estimates of prediction reliability by providing quantified uncertainty ranges that effectively address various aspects of data- and model-related uncertainty. Developing uncertainty-aware models caused no substantial loss of predictive performance compared to standard ML approaches without uncertainty quantification. Additionally, these models harnessed the full potential of available data, enabling robust predictions across a broader range of chemicals by accurately reflecting differences in prediction reliability. Chapter 5 applies this approach to predict human non-cancer toxicity points of departure (PODs) for >130,000 marketed chemicals, identifying chemical classes with high toxicity and significant prediction uncertainty. These results fill critical data gaps by providing predictions for many marketed chemicals with no prior estimates and guide future research to enhance predictions for chemical classes with high uncertainty, such as metals, inorganics, and macromolecules. Chapter 5 further explores practical challenges in applying digital tools to obtain robust toxicity characterization results through an illustrative case study aiming to identify safer building blocks for enzymatic amide bond synthesis. By propagating (semi)-quantitative input parameter uncertainties, the results revealed significant deviations between probabilistic best estimates and deterministic results, as well as large uncertainties that hindered reliable identification of toxicity impact differences among similar chemicals. This underscored the importance of quantifying uncertainty in input data predictions to obtain robust conclusions in comparative chemical assessments.This PhD project built the foundation for developing fit-for-purpose digital prediction tools for chemical toxicity characterization, offering a comprehensive view on critical gaps and strategies for addressing them. By prioritizing relevant input parameters and establishing the chemical target domain as a benchmark for the predictive capabilities required from ML-based approaches, the presented approaches guide future efforts for data curation and ML model development that can systematically enhance the availability and robustness of chemical toxicity characterization results. As an essential aspect, this project demonstrated the development of uncertainty-aware ML and its importance for effectively integrating predictions in chemical toxicity characterization to obtain robust conclusions from prediction-based chemical assessments. This approach also significantly improved data availability across globally marketed chemicals, as it allowed providing predictions with quantified uncertainty for highly diverse chemicals. Applying it to fill data gaps for other critical input parameters holds practical relevance for industry and academia, as it would significantly improve the availability and robustness of chemical risk and impact assessments, opening new possibilities for comparing alternatives across marketed chemicals and new chemical designs to create safer and more sustainable products. This PhD project thereby made a substantial contribution to the fields of chemical impact and risk assessment to support effective chemical management in minimizing chemical impacts on humans and ecosystems

Degradation mechanisms in PBSAT nets immersed in seawater

Fishing gears are known to continue fishing after being abandoned, lost, or discarded through a phenomenon called ghost fishing. After this ghost fishing period, disintegrated nets contribute to plastic pollution. Biodegradable nets could be an alternative to conventional nets to reduce ghost fishing but must strike a delicate balance between durability and degradation. This study evaluates the seawater degradation of a net made of polybutylene(succinate-co-adipate-co-terepthalate) (PBSAT) at several scales: monofilament, knot, and net. Mechanical testing was used to monitor the strength at each scale during immersion at several temperatures: 4 °C, 15 °C, 25 °C, 40 °C. Steric exclusion chromatography (SEC), scanning electron microscopy (SEM) and X-ray tomography were used to investigate degradation processes. While no degradation was observed for samples immersed for 240 days at 4 °C, hydrolysis led to embrittlement at 40 °C. Biotic degradation was observed at both 15 °C and 25 °C with distinct degradation patterns and bacteria shapes. At both temperatures, the degradation was accelerated in the knot, leading to an unusable net after 240 days at 15 °C while no loss of strength was detected at the monofilament scale. These findings suggest that the durability of the knot is critical for successful development of a biodegradable polymer for application in gillnets

Simultaneous ammonia and nitrate removal by novel integrated partial denitrification-anaerobic ammonium oxidation-bioelectrochemical system

The current study explored the performance of an integrated partial denitrification-anaerobic ammonium oxidation (anammox)-bioelectrochemical system on simultaneous removal of ammonia nitrogen and nitrate nitrogen. Different operational conditions were selected to optimize critical parameters of the process for improving nitrogen removal. The results indicated that more than 90 % of total inorganic nitrogen removal efficiency was achieved under the optimal conditions: ammonia nitrogen/nitrate nitrogen ratio of 1:2, external resistance of 200 Ω and inoculation volume ratio of anammox bacteria/denitrifying at 2:1. Improved nitrogen removal under the optimal conditions were confirmed by microbial community changes (Candidatus Brocadia and Thiobacillus) and enhanced of nitrogen metabolism-related genes (hao, hzsA/C and hdh). Increases of Limnobacter indicated an enhanced electron transfer efficiency. Overall, high-efficiency and stable nitrogen removal efficiency without nitrite nitrogen accumulation could be achieved by the integrated system under the optimal conditions, providing novel insights for simultaneous treatment of domestic wastewater and groundwater

Effect of tool revolutionary pitch on heat transfer and material flow in Al/steel friction stir lap welding

The motion condition of a friction stir welding tool significantly affects weld formation and quality in dissimilar joining between Al alloy and steel owing to its direct effect on thermo-mechanical condition during the process. In this study, a finite element simulation based on the coupled Eulerian–Lagrangian formulation was used to investigate the thermomechanical field evolutions in the friction stir lap welding of Al and steel, focusing on the effect of tool revolutionary pitch on thermal energy generation and material flow. Simulation results indicated that an increasing revolutionary pitch increased the proportion of heat input generated by plastic deformation, exhibiting an approximately linear relationship under the adopted welding parameters. With a fixed revolutionary pitch, a high- parameter-matching of rotational created higher welding temperatures. A lower revolutionary pitch resulted in higher welding temperature, leading to a thicker intermetallic layer at the Al/steel interface. However, a high revolutionary pitch under high-matching parameters significantly weakened the mixing of two materials behind the pin by decreasing material migration from the Al alloy and steel at the pin bottom on the restraining side, due to reduced temperature and increased deformation resistance. A parabolic mathematical correlation between interfacial strength and revolutionary pitch was observed, suggesting that maintaining a revolutionary pitch within the range of 0.4 to 1.2 mm/rev could produce a robust Al/steel lapped interface with joining efficiencies of approximately 91% or higher compared to the Al alloy base metal

Autotrophic degradation of sulfamethoxazole using sulfate-reducing biocathode in microbial photo-electrolysis system

Sulfamethoxazole is a representative of sulfonamide antibiotic pollutants. This study aims to investigate the degradation pathways of sulfamethoxazole and the response of microbial communities using the autotrophic biocathode in microbial photo-electrolysis systems (MPESs). Sulfamethoxazole with an initial concentration of 2 mg L−1 was degraded into small molecule propanol within 6 h with the biocathode. Elemental sulfur (S0) was detected in the cathode chamber, accounting for 57 % of the removed sulfate. The conversion from sulfate to S0 indicated that autotrophic microorganisms might adopt a novel pathway for sulfamethoxazole removal in the MPES. In the abiotic cathode, sulfamethoxazole degradation rate was 0.09 mg L−1 h−1 with the electrochemistry process. However, sulfamethoxazole was converted to products that still contain benzene rings, including p-aminothiophenol, 3-amino-5-methylisoxazole, and sulfonamide. The microbial community analysis indicated that the synergistic interaction of Desulfovibrio and Acetobacterium promoted the autotrophic degradation of sulfamethoxazole. The results suggested that autotrophic microorganisms may play an important role in the environmental transformation of sulfamethoxazole.

Associations between bedroom environment and sleep quality when sleeping less or more than 6h:A cross sectional study during summer

A cross-sectional study was conducted in 50 bedrooms in Shanghai, each for one week during summer, to investigate the effects of the bedroom environment on sleep quality. Sleep quality was recorded with a wrist-worn sleep tracker and assessed with a questionnaire. The measurements from 168 person-nights were analysed after excluding unreliable data. The overnight means of bedroom temperature, relative humidity, CO2 and PM2.5 concentrations, which were continuously measured were between 21 to 30 °C, 43 to 86 %, 375 to 2756 ppm, and <1 to 43 μg/m3, respectively. Sleep duration in 36.3 % of the nights was less than 6 h, which is too short according to the recommendation of the American Sleep Foundation. The nights were then classified into two categories, with sleep duration above or below 6 h. The results show that the sleep quality in the short-sleep category was more sensitive to the bedroom environment. In the longer-sleep duration category, light sleep decreased with higher humidity and increased with higher PM2.5 concentration. In the short-sleep duration category, all the main sleep quality parameters including sleep efficiency and the duration and percentage of deep and rapid eye movement sleep stage decreased, and time spent awake increased with increased bedroom temperature, humidity, CO2 and PM2.5 concentration. The occupants who used air conditioning had a lower indoor temperature and humidity at night; opening windows decreased the indoor CO2 concentration but increased the PM2.5 concentration. These results suggest the need for alternative solutions to natural ventilation during summer in dwellings in Shanghai

Acid doped branched poly(biphenyl pyridine) membranes for high temperature proton exchange membrane fuel cells and vanadium redox flow batteries

Both high temperature proton exchange membrane fuel cell (HT-PEMFC) and vanadium redox flow battery (VRFB) are represented as two advanced energy conversion and energy storage devices. They have a same core component of the separator membrane, which still faces several intractable scientific and industrial issues. For HT-PEMFC, the increase in conductivity is normally as the expense of mechanical strength; while for VRFB, the improvement in proton transport always brings in serious vanadium ion crossover. Meanwhile, the membrane also should possess an excellent chemical stability towards the attack by radicals or high valence vanadium ions. The above questions can be well solved by the preparation of triphenylbenzene (TPB) branched poly(biphenyl-4-acetylpyridine) membranes (x%TPB-PBAP), which are synthesized by one-step Friedel-Crafts polymerization. Amounts of alkaline pyridine groups equip x%TPB-PBAP membranes with good phosphoric acid and sulfonic acid absorption capability, resulting in high proton conductivity in both HT-PEMFC and VRFB. Meanwhile, the construction of the branched structure, i.e. a kind of covalently crosslinked network, can improve the mechanical strength and chemical stability. Consequently, the 1.5 %TPB-PBAP membrane displays large potential in both HT-PEMFC and VRFB. A single H2-O2 cell based on the 1.5 %TPB-PBAP/263 %PA membrane shows a peak power density of 1010 mW cm−2 at 180 °C without any back pressure. Meanwhile, the VRFB based on above membrane also depicts better battery efficiencies and cycle durability than that with Nafion 212

Spatial estimation of unidirectional wave evolution based on ensemble data assimilation

With the limitation of the high sensitivity of nonlinear models to initial conditions, the accurate estimation of wave spatial evolution is difficult to perform at a long distance. At this stage, a helpful approach is to improve the accuracy and robustness of the model through data assimilation technique. A robust data assimilation framework is developed by coupling ensemble Kalman filtering (EnKF) with the nonlinear wave model. The spatial evolution is obtained by numerically integrating the viscous modified Nonlinear Schrödinger (MNLS) equation. The performance of the EnKF-MNLS coupled framework is tested using synthetic data and laboratory measurements. The synthetic data is generated by the MNLS simulation superposing the Gaussian noise. In the synthetic cases, the estimated wave envelopes agree well with the clean solution. The results of laboratory experiments indicate that the EnKF-MNLS framework can improve the accuracy of wave forecasts compared to noised MNLS simulations. This study aims to enhance the noise resistance of the nonlinear wave model in spatial evolution and improve the accuracy of the model forecast

A novel temporal–spatial graph neural network for wind power forecasting considering blockage effects

Wind Power Forecasting is crucial for the operational security, stability, and economic efficiency of the power grid, yet it faces significant accuracy challenges due to the variable nature of wind energy and complex interactions within wind farms. This study introduces a novel neural network model specifically designed for Wind Power Forecasting, incorporating both a gated dilated inception network and a graph neural network. This innovative approach enables the concurrent analysis of temporal and spatial features of wind energy, significantly enhancing the forecasting accuracy. A pivotal feature of this model is its unique mechanism to compute the mutual influence between wind turbines, with a particular focus on the blockage effect, a key factor in turbine interactions. The model's efficacy is validated using a real-world dataset, targeting a 48-hour prediction horizon. The experimental outcomes demonstrate that this model achieves superior performance compared to state-of-the-art methods, with a notable improvement of 6.87% in Root Mean Square Error and 8.77% in Mean Absolute Error. This study not only highlights the model's enhanced forecasting capabilities but also emphasizes the importance of integrating spatial and temporal dynamics in wind farms for improving Wind Power Forecasting accuracy.</p

High Pressure Ammonia/Methanol Oxidation up to 100 atm

High pressure ammonia/methanol oxidation and NOx formations were investigated using a recently developed supercritical pressure jet-stirred reactor (SP-JSR) at 20 and 100 atm with temperatures between 550 and 950 K and equivalence ratios of 0.138 and 1.15. The experimental results show that NH3 oxidation at high pressure is significantly accelerated by the active OH radicals produced from CH3OH oxidation. Furthermore, the kinetic interactions between NH3 and CH3OH are governed mainly by the reactions CH3OH + NH2 = CH2OH + NH3, CH3OH + NH2 = CH3O + NH3, and CH2O + NH2 = HCO + NH3. A HP-Mech model for high-pressure NH3/CH3OH oxidation was developed in this study. It consists of the most recent NH3 and CH3OH models including some new reactions and updated rate constants from the literature as well as NH3-CH3OH interactions where rate constants of CH3OH + NH2 = CH2OH + NH3, CH3OH + NH2 = CH3O + NH3, NH2 + CH2O = NH3 + HCO, and NH2 + CH2O = NH2CHO + H were theoretically calculated in this study. Our model with these updates improves the prediction for the measured N2O/NOx temperature dependence at 100 atm. In addition, the reaction pathway and sensitivity analysis show that N2O/NOx/HONO interactions with HO2 are very important, especially for a fuel-lean mixture at 100 atm. The HONO mole fraction for the fuel-lean mixture at 100 atm was then measured by off-axis integrated cavity output spectroscopy (ICOS) at wavenumber of 6638.26 cm−1. The experimental data show a significant HONO formation at intermediate temperature that is strongly underpredicted by numerical simulation at 100 atm. Therefore, the HONO related reactions with notable uncertainty at high pressure such as NO + OH (+M) = HONO (+M) and H2NO + NO2 = HONO + HNO need deeper exploration in the future