Knowledge-driven Optimization Research Articles

Unveiling the kinetics of CO2 reduction in aprotic electrolyte: The critical role of adsorption

Aprotic organic electrolytes for electrocatalytic CO2 reduction (CO2R) offer a promising yet little-explored alternative to avoid the selectivity issues of alkaline media. We present a model-assisted study of the local reaction environment at a Cu cathode in aprotic organic electrolytes to determine the kinetics of the CO2R and the kinetic limitations and reveal their dependence on reaction conditions. The adsorption of CO2 is identified as the rate-determining step, as opposed to the often-assumed electron transfer during radical anion formation. Unlike alkaline media, there is no transport limitation in aprotic media. Furthermore, our model predicts two strategies to overcome the adsorption limitation: (i) by increasing surface roughness and (ii) by elevating pressure. The findings highlight the critical role of CO2 adsorption on CO2R performance and enable knowledge-driven optimization of reaction conditions and electrocatalysts.

Deciphering the contributing motifs of reconstructed cobalt (II) sulfides catalysts in Li-CO2 batteries

Developing highly efficient catalysts is significant for Li-CO2 batteries. However, understanding the exact structure of catalysts during battery operation remains a challenge, which hampers knowledge-driven optimization. Here we use X-ray absorption spectroscopy to probe the reconstruction of CoSx (x = 8/9, 1.097, and 2) pre-catalysts and identify the local geometric ligand environment of cobalt during cycling in the Li-CO2 batteries. We find that different oxidized states after reconstruction are decisive to battery performance. Specifically, complete oxidation on CoS1.097 and Co9S8 leads to electrochemical performance deterioration, while oxidation on CoS2 terminates with Co-S4-O2 motifs, leading to improved activity. Density functional theory calculations show that partial oxidation contributes to charge redistributions on cobalt and thus facilitates the catalytic ability. Together, the spectroscopic and electrochemical results provide valuable insight into the structural evolution during cycling and the structure-activity relationship in the electrocatalyst study of Li-CO2 batteries.

Role of Fe decoration on the oxygen evolving state of Co3O4 nanocatalysts.

The production of green hydrogen through alkaline water electrolysis is the key technology for the future carbon-neutral industry. Nanocrystalline Co3O4 catalysts are highly promising electrocatalysts for the oxygen evolution reaction and their activity strongly benefits from Fe surface decoration. However, limited knowledge of decisive catalyst motifs at the atomic level during oxygen evolution prevents their knowledge-driven optimization. Here, we employ a variety of operando spectroscopic methods to unveil how Fe decoration increases the catalytic activity of Co3O4 nanocatalysts as well as steer the (near-surface) active state formation. Our study shows a link of the termination-dependent Fe decoration to the activity enhancement and a significantly stronger Co3O4 near-surface (structural) adaptation under the reaction conditions. The near-surface Fe- and Co-O species accumulate an oxidative charge and undergo a reversible bond contraction during the catalytic process. Moreover, our work demonstrates the importance of low coordination surface sites on the Co3O4 host to ensure an efficient Fe-induced activity enhancement, providing another puzzle piece to facilitate optimized catalyst design.

(Invited) Size, Composition Effects and Active State Formation of Nanostructured Cobalt and Iron-Cobalt Oxide-Based Catalysts during Oxygen Evolution Reaction

The efficient water electrolysis is a key technology to establish a CO2-neutral, electrochemical (green) hydrogen production. Nonetheless, the electrocatalysts’ near-surface structure during the anodic oxygen evolution reaction (OER) is still largely unknown, which hampers knowledge-driven optimization. Here, we provide quantitative near-surface structural insights into oxygen-evolving CoOX(OH)Y nanoparticles by tracking their size-dependent catalytic activity down to 1 nm and their structural adaptation to OER conditions combining operando X-ray absorption spectroscopy and density functional theory calculations. We uncover a superior intrinsic OER activity of sub-5 nm nanoparticles and a size-dependent oxidation leading to a near-surface Co-O bond contraction and charge redistribution during OER.I will also report on the role of Fe in the performance of Co-oxide-based electrocalysts. By comparing epitaxial Co3O4(111), Co1+δFe2-δO4(111) (|δ|<=0.2), and Fe3O4(111) thin film electrocatalysts combining quasi in-situ preparation and characterization in ultra-high vacuum (UHV) with electrochemistry experiments. Interestingly, Co1+δFe2-δ O4(111) was found to be up to five times more active than Co3O4(111) and Fe3O4(111) with the activity depending acutely on the Co/Fe concentration ratio. Under OER conditions, all three oxides are covered by oxyhydroxide but the characteristics of this overlayer, including its thickness, stability, structural and chemical evolution was found to depend on the Fe content.

Knowledge-Driven Multi-Objective Optimization for Reconfigurable Manufacturing Systems

Current market requirements force manufacturing companies to face production changes more often than ever before. Reconfigurable manufacturing systems (RMS) are considered a key enabler in today’s manufacturing industry to cope with such dynamic and volatile markets. The literature confirms that the use of simulation-based multi-objective optimization offers a promising approach that leads to improvements in RMS. However, due to the dynamic behavior of real-world RMS, applying conventional optimization approaches can be very time-consuming, specifically when there is no general knowledge about the quality of solutions. Meanwhile, Pareto-optimal solutions may share some common design principles that can be discovered with data mining and machine learning methods and exploited by the optimization. In this study, the authors investigate a novel knowledge-driven optimization (KDO) approach to speed up the convergence in RMS applications. This approach generates generalized knowledge from previous scenarios, which is then applied to improve the efficiency of the optimization of new scenarios. This study applied the proposed approach to a multi-part flow line RMS that considers scalable capacities while addressing the tasks assignment to workstations and the buffer allocation problems. The results demonstrate how a KDO approach leads to convergence rate improvements in a real-world RMS case.

Size effects and active state formation of cobalt oxide nanoparticles during the oxygen evolution reaction

Water electrolysis is a key technology to establish CO2-neutral hydrogen production. Nonetheless, the near-surface structure of electrocatalysts during the anodic oxygen evolution reaction (OER) is still largely unknown, which hampers knowledge-driven optimization. Here using operando X-ray absorption spectroscopy and density functional theory calculations, we provide quantitative near-surface structural insights into oxygen-evolving CoOx(OH)y nanoparticles by tracking their size-dependent catalytic activity down to 1 nm and their structural adaptation to OER conditions. We uncover a superior intrinsic OER activity of sub-5 nm nanoparticles and a size-dependent oxidation leading to a near-surface Co–O bond contraction during OER. We find that accumulation of oxidative charge within the surface Co3+O6 units triggers an electron redistribution and an oxyl radical as predominant surface-terminating motif. This contrasts the long-standing view of high-valent metal ions driving the OER, and thus, our advanced operando spectroscopy study provides much needed fundamental understanding of the oxygen-evolving near-surface chemistry.

Reaction Mechanism of Pd-Catalyzed "CO-Free" Carbonylation Reaction Uncovered by In Situ Spectroscopy: The Formyl Mechanism.

“CO‐free” carbonylation reactions, where synthesis gas (CO/H2) is substituted by C1 surrogate molecules like formaldehyde or formic acid, have received widespread attention in homogeneous catalysis lately. Although a broad range of organics is available via this method, still relatively little is known about the precise reaction mechanism. In this work, we used in situ nuclear magnetic resonance (NMR) spectroscopy to unravel the mechanism of the alkoxycarbonylation of alkenes using different surrogate molecules. In contrast to previous hypotheses no carbon monoxide could be found during the reaction. Instead the reaction proceeds via the C−H activation of in situ generated methyl formate. On the basis of quantitative NMR experiments, a kinetic model involving all major intermediates is built which enables the knowledge‐driven optimization of the reaction. Finally, a new reaction mechanism is proposed on the basis of in situ observed Pd‐hydride, Pd‐formyl and Pd‐acyl species.

Adaptive Knowledge-Driven Optimization for Architecting a Distributed Satellite System

As distributed satellite systems gain interest, there is a growing need for design tools that can identify system architectures with good trades in multiple metrics. Evolutionary algorithms have shown promise as effective design-support tools but are computationally inefficient because they require evaluating many architectures before identifying ones with good tradeoffs. Knowledge-driven optimization aims to improve evolutionary algorithms by including a data-mining algorithm that learns design features common in the best architectures found during the optimization. These design features can guide the remainder of the optimization but should be used carefully to prevent misleading the search. This work presents a new knowledge-driven optimization framework, knowledge-driven optimization (KDO)/adaptive operator selection (AOS), that monitors how well the design features guide the search toward good architectures and focuses on using the best features. If none are beneficial to the optimization, KDO/AOS adapts its search strategy to explore regions of the trade space not captured by the design features. KDO/AOS is demonstrated on an architecture design problem for a distributed satellite system, and its adaptive search strategy enables it to discover high-quality architectures with over 800 fewer evaluations when compared with a conventional evolutionary algorithm. KDO/AOS is also more robust than other knowledge-driven optimization algorithms to inaccuracies from data mining.

Incorporating expert knowledge into evolutionary algorithms with operators and constraints to design satellite systems

Knowledge-intensive evolutionary algorithms (EA) and knowledge-driven optimization algorithms that leverage problem- or domain-specific knowledge have been shown to discover high-quality solutions with fewer function evaluations than knowledge-independent EAs. Knowledge-intensive EAs apply the available knowledge through (1) knowledge-dependent operators that modify specific decision variables by biasing them towards or away from predetermined values or (2) knowledge-dependent constraints that penalize or eliminate solutions that are inconsistent with the provided knowledge. While both knowledge-dependent operators and knowledge-dependent constraints have shown promise in improving the search performance of an EA, there are no known experiments comparing their efficacy. To address the lack of such comparative experiments, this paper benchmarks one EA using knowledge-dependent operators and two EAs using knowledge-dependent constraints against an analogous knowledge-independent EA on a design problem for a climate-monitoring satellite system. Each EA is evaluated for its ability to attain high-quality solutions with the fewest possible number of function evaluations. In addition, we analyze the extent to which each method can focus on applying the knowledge that improves the search performance, handle conflicting information that suggests improving solutions with opposing modifications, and balance the exploitation of the available knowledge with the exploration of the tradespace to prevent premature convergence on local optima. The results show promise for EAs applying knowledge-dependent operators with an adaptive operator selection strategy and reveal some limitations of methods that adaptively apply knowledge-dependent constraints.

Rational Design of a Water-Soluble, Lipid-Compatible Fluorescent Probe for Cu(I) with Sub-Part-Per-Trillion Sensitivity.

Fluorescence probes represent an attractive solution for the detection of the biologically important Cu(I) cation; however, achieving a bright, high-contrast response has been a challenging goal. Concluding from previous studies on pyrazoline-based fluorescent Cu(I) probes, the maximum attainable fluorescence contrast and quantum yield were limited due to several non-radiative deactivation mechanisms, including ternary complex formation, excited state protonation, and colloidal aggregation in aqueous solution. Through knowledge-driven optimization of the ligand and fluorophore architectures, we overcame these limitations in the design of CTAP-3, a Cu(I)-selective fluorescent probe offering a 180-fold fluorescence enhancement, 41% quantum yield, and a limit of detection in the sub-part-per-trillion concentration range. In contrast to lipophilic Cu(I)-probes, CTAP-3 does not aggregate and interacts only weakly with lipid bilayers, thus maintaining a high contrast ratio even in the presence of liposomes.

Mapping internal structure of coal by confocal micro-Raman spectroscopy and scanning microwave microscopy

Structural complexity and variability of the chemical properties define technological applicability of coal and demand increasing accuracy and spatial resolution from the techniques used for coal characterization for development of new, clean, and efficient technologies of coal utilization. Here, we combined spatially-resolved reflectometry, fluorescence, and confocal micro-Raman spectroscopy with high-resolution scanning probe microwave imaging to achieve a nondestructive sub-100-nm spatial resolution mapping of coal structure. It was found that this approach allows for high spatial resolution identification of individual elements in coal architecture, thus potentially generating valuable input for knowledge-driven optimization and design of coal utilization processes.

Metagenomes obtained by ‘deep sequencing’ – what do they tell about the enhanced biological phosphorus removal communities?

Metagenomics enables studies of the genomic potential of complex microbial communities by sequencing bulk genomic DNA directly from the environment. Knowledge of the genetic potential of a community can be used to formulate and test ecological hypotheses about stability and performance. In this study deep metagenomics and fluorescence in situ hybridization (FISH) were used to study a full-scale wastewater treatment plant with enhanced biological phosphorus removal (EBPR), and the results were compared to an existing EBPR metagenome. EBPR is a widely used process that relies on a complex community of microorganisms to function properly. Insight into community and species level stability and dynamics is valuable for knowledge-driven optimization of the EBPR process. The metagenomes of the EBPR communities were distinct compared to metagenomes of communities from a wide range of other environments, which could be attributed to selection pressures of the EBPR process. The metabolic potential of one of the key microorganisms in the EPBR process, Accumulibacter, was investigated in more detail in the two plants, revealing a potential importance of phage predation on the dynamics of Accumulibacter populations. The results demonstrate that metagenomics can be used as a powerful tool for system wide characterization of the EBPR community as well as for a deeper understanding of the function of specific community members. Furthermore, we discuss and illustrate some of the general pitfalls in metagenomics and stress the need of additional DNA extraction independent information in metagenome studies.