Active learning streamlines development of high performance catalysts for higher alcohol synthesis.

Developing efficient catalysts for syngas-based higher alcohol synthesis (HAS) remains a formidable research challenge. The chain growth and CO insertion requirements demand multicomponent materials, whose complex reaction dynamics and extensive chemical space defy catalyst design norms. We present an alternative strategy by integrating active learning into experimental workflows, exemplified via the FeCoCuZr catalyst family. Our data-aided framework streamlines navigation of the extensive composition and reaction condition space in 86 experiments, offering >90% reduction in environmental footprint and costs over traditional programs. It identifies the Fe65Co19Cu5Zr11 catalyst with optimized reaction conditions to attain higher alcohol productivities of 1.1 gHA h-1 gcat-1 under stable operation for 150 h on stream, a 5-fold improvement over typically reported yields. Characterization reveals catalytic properties linked to superior activities despite moderate higher alcohol selectivities. To better reflect catalyst demands, we devise multi-objective optimization to maximize higher alcohol productivity while minimizing undesired CO2 and CH4 selectivities. An intrinsic trade-off between these metrics is uncovered, identifying Pareto-optimal catalysts not readily discernible by human experts. Finally, based on feature-importance analysis, we formulate data-informed guidelines to develop performance-specific FeCoCuZr systems. This approach goes beyond existing HAS catalyst design strategies, is adaptable to broader catalytic transformations, and fosters laboratory sustainability.

Active Learning-Based Guided Synthesis of Engineered Biochar for CO2 Capture.

Biomass waste-derived engineered biochar for CO2 capture presents a viable route for climate change mitigation and sustainable waste management. However, optimally synthesizing them for enhanced performance is time- and labor-intensive. To address these issues, we devise an active learning strategy to guide and expedite their synthesis with improved CO2 adsorption capacities. Our framework learns from experimental data and recommends optimal synthesis parameters, aiming to maximize the narrow micropore volume of engineered biochar, which exhibits a linear correlation with its CO2 adsorption capacity. We experimentally validate the active learning predictions, and these data are iteratively leveraged for subsequent model training and revalidation, thereby establishing a closed loop. Over three active learning cycles, we synthesized 16 property-specific engineered biochar samples such that the CO2 uptake nearly doubled by the final round. We demonstrate a data-driven workflow to accelerate the development of high-performance engineered biochar with enhanced CO2 uptake and broader applications as a functional material.

Language models and protocol standardization guidelines for accelerating synthesis planning in heterogeneous catalysis.

Synthesis protocol exploration is paramount in catalyst discovery, yet keeping pace with rapid literature advances is increasingly time intensive. Automated synthesis protocol analysis is attractive for swiftly identifying opportunities and informing predictive models, however such applications in heterogeneous catalysis remain limited. In this proof-of-concept, we introduce a transformer model for this task, exemplified using single-atom heterogeneous catalysts (SACs), a rapidly expanding catalyst family. Our model adeptly converts SAC protocols into action sequences, and we use this output to facilitate statistical inference of their synthesis trends and applications, potentially expediting literature review and analysis. We demonstrate the model's adaptability across distinct heterogeneous catalyst families, underscoring its versatility. Finally, our study highlights a critical issue: the lack of standardization in reporting protocols hampers machine-reading capabilities. Embracing digital advances in catalysis demands a shift in data reporting norms, and to this end, we offer guidelines for writing protocols, significantly improving machine-readability. We release our model as an open-source web application, inviting a fresh approach to accelerate heterogeneous catalysis synthesis planning.

Identifying Descriptors for Promoted Rhodium-Based Catalysts for Higher Alcohol Synthesis via Machine Learning.

Rhodium-based catalysts offer remarkable selectivities toward higher alcohols, specifically ethanol, via syngas conversion. However, the addition of metal promoters is required to increase reactivity, augmenting the complexity of the system. Herein, we present an interpretable machine learning (ML) approach to predict and rationalize the performance of Rh-Mn-P/SiO2 catalysts (P = 19 promoters) using the open-source dataset on Rh-catalyzed higher alcohol synthesis (HAS) from Pacific Northwest National Laboratory (PNNL). A random forest model trained on this dataset comprising 19 alkali, transition, post-transition metals, and metalloid promoters, using catalytic descriptors and reaction conditions, predicts the higher alcohols space-time yield (STYHA) with an accuracy of R 2 = 0.76. The promoter's cohesive energy and alloy formation energy with Rh are revealed as significant descriptors during posterior feature-importance analysis. Their interplay is captured as a dimensionless property, coined promoter affinity index (PAI), which exhibits volcano correlations for space-time yield. Based on this descriptor, we develop guidelines for the rational selection of promoters in designing improved Rh-Mn-P/SiO2 catalysts. This study highlights ML as a tool for computational screening and performance prediction of unseen catalysts and simultaneously draws insights into the property-performance relations of complex catalytic systems.

Prediction of Soil Heavy Metal Immobilization by Biochar Using Machine Learning.

Biochar application is a promising strategy for the remediation of contaminated soil, while ensuring sustainable waste management. Biochar remediation of heavy metal (HM)-contaminated soil primarily depends on the properties of the soil, biochar, and HM. The optimum conditions for HM immobilization in biochar-amended soils are site-specific and vary among studies. Therefore, a generalized approach to predict HM immobilization efficiency in biochar-amended soils is required. This study employs machine learning (ML) approaches to predict the HM immobilization efficiency of biochar in biochar-amended soils. The nitrogen content in the biochar (0.3-25.9%) and biochar application rate (0.5-10%) were the two most significant features affecting HM immobilization. Causal analysis showed that the empirical categories for HM immobilization efficiency, in the order of importance, were biochar properties > experimental conditions > soil properties > HM properties. Therefore, this study presents new insights into the effects of biochar properties and soil properties on HM immobilization. This approach can help determine the optimum conditions for enhanced HM immobilization in biochar-amended soils.

Wet wastes to bioenergy and biochar: A critical review with future perspectives.

The ever-increasing rise in the global population coupled with rapid urbanization demands considerable consumption of fossil fuel, food, and water. This in turn leads to energy depletion, greenhouse gas emissions and wet wastes generation (including food waste, animal manure, and sewage sludge). Conversion of the wet wastes to bioenergy and biochar is a promising approach to mitigate wastes, emissions and energy depletion, and simultaneously promotes sustainability and circular economy. In this study, various conversion technologies for transformation of wet wastes to bioenergy and biochar, including anaerobic digestion, gasification, incineration, hydrothermal carbonization, hydrothermal liquefaction, slow and fast pyrolysis, are comprehensively reviewed. The technological challenges impeding the widespread adoption of these wet waste conversion technologies are critically examined. Eventually, the study presents insightful recommendations for the technological advancements and wider acceptance of these processes by establishing a hierarchy of factors dictating their performance. These include: i) life-cycle assessment of these conversion technologies with the consideration of reactor design and catalyst utilization from lab to plant level; ii) process intensification by integrating one or more of the wet waste conversion technologies for improved performance and sustainability; and iii) emerging machine learning modeling is a promising strategy to aid the product characterization and optimization of system design for the specific to the bioenergy or biochar application.

Pyrolysis of waste surgical masks into liquid fuel and its life-cycle assessment.

Pyrolysis of the middle layer of a surgical mask (MLM) and inner and outer layers of a surgical mask (IOM) was performed to assess their potential valorization as waste-to-energy feedstocks, and the characteristics of the resulting products were investigated. Pyrolysis of the main organics in waste surgical masks occurred at a very narrow temperature range of 456-466 °C. The main product was carbon-rich and oxygen-deficient liquid oil with a high heating value (HHV) of 43.5 MJ/kg. From the life-cycle perspective, environmental benefits and advantages of this upcycling approach were verified compared with conventional waste management approaches. This study advocated the potential application of waste surgical masks as feedstocks for fuels and energy, which is beneficial to mitigate plastic pollution and achieve sustainable plastic waste-to-energy upcycling, simultaneously.

Applied Machine Learning for Prediction of CO2 Adsorption on Biomass Waste-Derived Porous Carbons.

Biomass waste-derived porous carbons (BWDPCs) are a class of complex materials that are widely used in sustainable waste management and carbon capture. However, their diverse textural properties, the presence of various functional groups, and the varied temperatures and pressures to which they are subjected during CO2 adsorption make it challenging to understand the underlying mechanism of CO2 adsorption. Here, we compiled a data set including 527 data points collected from peer-reviewed publications and applied machine learning to systematically map CO2 adsorption as a function of the textural and compositional properties of BWDPCs and adsorption parameters. Various tree-based models were devised, where the gradient boosting decision trees (GBDTs) had the best predictive performance with R2 of 0.98 and 0.84 on the training and test data, respectively. Further, the BWDPCs in the compiled data set were classified into regular porous carbons (RPCs) and heteroatom-doped porous carbons (HDPCs), where again the GBDT model had R2 of 0.99 and 0.98 on the training and 0.86 and 0.79 on the test data for the RPCs and HDPCs, respectively. Feature importance revealed the significance of adsorption parameters, textural properties, and compositional properties in the order of precedence for BWDPC-based CO2 adsorption, effectively guiding the synthesis of porous carbons for CO2 adsorption applications.

Optimization, kinetics and radioprotective potential of a heteropolysaccharide from L. rhamnosus RVPI.

The probiotic potential of Lactobacillus rhamnosus RVP1 isolated from Sardinella longiceps was investigated in vitro. The bacterium exhibited highest tolerance at low pH, high bile salt concentration and demonstrated good antioxidant activity, hydrophobicity and inhibited both gram-negative and gram-positive indicator bacteria. To aid in process design and to unravel the fermentation kinetics, response surface methodology was devised to optimize the EPS production from L. rhamnosus and mechanistic models were developed to describe the fermentation kinetics. The optimum pH, dextrose and peptone concentrations for EPS production were 7.07, 19.995 g/L and 23.4 g/L, respectively, with a predicted yield of 724 mg/L. The actual yield under these conditions was 708±29 mg/L which was within the 95% confidence interval. The simulated mechanistic model fit the experimental values with a high degree of correlation with R2 = 0.99, 0.96 and 0.97 for the logistic growth, substrate consumption and EPS production and degradation curves respectively. The kinetic constants µ_max = 0.29 hr-1 , Xmax = 3.44 g/L, kf = 348 mg of EPS/ g of dry biomass and kd = 0.53 hr-1 were derived from the model. The EPS administration improved the survival of irradiated mice by 50% proving it radioprotective potential and showed positive effects on structural integrity of intestinal tissue. This article is protected by copyright. All rights reserved.