Rational Experimental Design Research Articles

Theoretical insight into the mechanism of Li-mediated nitrogen reduction reaction by density functional theory

Lithium-mediated electrochemical nitrogen reduction reaction (NRR) as an alternative to the Haber-Bosch process has attracted increasing attention because of its high faradaic efficiency and reproducibility. However, the limited understanding of the mechanism has hampered further improvement of its catalytic performance. This work has endeavored to study the process of Li-mediated NRR and its underlying mechanism using density functional theory. It is founded that the Li6N2, Li7N2 atom groups, Li3N (001) and Li3N (110) layer stably exist on the deposited Li (001) layer. The replacing model has been established to describe the hydrogenation process with ethanol as a proton source, revealing that the replacement between Li and H atom is a spontaneously thermal process. Based on the replacing mechanism, the structure of interface and coverage rate of reactive sites are the two main factors that determine the ammonia formation. These findings further our understanding of Li-mediated NRR mechanism and will be helpful for the rational design of experiments of Li-mediated NRR.

Forecasting short-term methane based on corrected numerical weather prediction outputs

Methane (CH4) represents a significant greenhouse gas, and the control of its emissions is crucial in impacting global climate change. Accurate forecasting of CH4 emissions is important for identifying future sources of high CH4 emissions, rational design of experiments, and information gathering. This study presented a method to improve numerical weather prediction (NWP) outputs using the Extreme Gradient Boosting (XGB) algorithm. In contrast to previous conventional correction methods, the XGB model greatly enhances the accuracy and stability of the NWP outputs. Short-term (1–3 d) CH4 forecasting models were built for 53 stations worldwide using three machine learning algorithms: Convolutional Neural Network (CNN), Random Forest (RF), and XGB. The models were validated with flux data from the stations. The results showed that the CNN model performed best in forecasting CH4 for all lead times (mean NRMSE = 0.34), followed by the RF model (mean NRMSE = 0.68) and the XGB model performed worst (mean NRMSE = 0.74). For land cover types (e.g., savanna (WSA) and forest (EBF)), the accuracy of the model may be greater for 2 d lead times than for 1 d lead times, which is closely related to the regional climate and terrain conditions. For lead times, the CNN model achieves high CH4 forecast accuracy at 1–2 d lead times. In addition, the RF and XGB models with 1 d lead times exhibit significant overestimation at most stations in North America (PBIAS>0.5). This work quantified the performance of machine learning models for correcting NWP outputs, providing a technical approach to the correction of NWP outputs; and assessed the performance of deep learning models for forecasting short-term CH4, providing a reference for model selection for CH4 forecasting.

AMMI and GGE biplot analysis for genotype × environment interactions affecting the yield and quality characteristics of sugar beet.

Sugar beet, an important sugar crop, contributes significantly to the world's sugar production. However, genotype-environment interactions (GEI) often affect the quality characteristics of sugar beet. Hence, understanding the effects of GEI on sugar beet quality can aid in identifying high-quality genotypes that can adapt to different environments. Traditional variance analysis can only be used to examine the yield of a variety and not its specific adaptability to specific conditions. Therefore, more comprehensive analytical methods are required to evaluate the characteristics of the variety under specific environments. Additive main effects and multiplicative interaction (AMMI) and genotype main effect and genotype × environment interaction (GGE) biplot models can be employed to comprehensively evaluate different varieties and address the drawbacks associated with a single evaluation method. Moreover, these models also allow us to explore new varieties more objectively and comprehensively. In this study, the adaptability and stability of 16 sugar beet varieties, in terms of yield and sugar content, were evaluated using AMMI and GGE biplot analysis in seven pilot projects undertaken in 2022. In the assessment of a small but significant proportion of the total GEI variance for the two qualitative traits (yield and sugar content), 80.58% of the variance was explained by the cumulative contribution of IPC1, IPC2, and IPC3. AMMI and GGE biplots clearly highlighted that KWS4207 (G3) exhibited high and stable quality. They also demonstrated that the experiments in Jalaid Banner (Inner Mongolia) (E7) were the most representative. Together, the results suggested that the comprehensive application of AMMI and GGE biplot analysis allowed for a more comprehensive, scientific, and effective evaluation of sugar beet varieties across different regions. The findings offer a theoretical basis for sugar beet breeding and could guide the rational design of experiments for testing new varieties of sugar beet.

Metabolic perturbation studies using a Nash Equilibrium model of liver machine perfusion: modeling oxidative stress and effect of glutathione supplementation

The current clinical standard of Static Cold Storage (SCS) which involves preservation on ice (about +4°C) in a hypoxic state limits storage to a few hours for metabolically active tissues such as the liver and the heart. This period of hypoxia during can generate superoxide and other free radicals from purine metabolism, a well-established component of ischemia/reperfusion injury (IRI). Machine perfusion is at the cutting edge of organ preservation, which provides a functional, oxygenated preservation modality that can avoid/attenuate IRI. In clinical application, perfusion usually follows a period of SCS. This presentation of oxygen following hypoxia can lead to superoxide and hydrogen peroxide generation, but machine perfusion also allows manipulation of the temperature profiles and supply of antioxidant treatments, which could be used to minimize such issues. However, metabolomic data is difficult to gather, and there are currently no mathematical models present to allow rational design of experiments or guide clinical practice. In this article, the effects of a gradual warming temperature policy and glutathione supplementation to minimize oxidative stress are studied. An optimal gradual warming temperature policy for mid-thermic machine perfusion of a liver metabolic model is determined using a combination of Nash Equilibrium and Monte Carlo optimization. Using this optimal gradual warming temperature policy, minimum GSH requirements to maintain hydrogen peroxide concentrations in the normal region are calculated using a different Monte Carlo optimization methodology. In addition, the dynamic behavior of key metabolites and cofactors are determined. Results show that the minimum GSH requirement increases and that the ratio of GSH/GSSG decreases with increasing hydrogen peroxide concentration. In addition, at high concentrations of hydrogen peroxide it is shown that cytochrome C undergoes dysfunction leading to a decrease in useful oxygen consumption and ATP synthesis from the electron transport chain and an overall reduction in the energy charge for the liver cells.

Interpretable and tractable models of transcriptional noise for the rational design of single-molecule quantification experiments.

The question of how cell-to-cell differences in transcription rate affect RNA count distributions is fundamental for understanding biological processes underlying transcription. Answering this question requires quantitative models that are both interpretable (describing concrete biophysical phenomena) and tractable (amenable to mathematical analysis). This enables the identification of experiments which best discriminate between competing hypotheses. As a proof of principle, we introduce a simple but flexible class of models involving a continuous stochastic transcription rate driving a discrete RNA transcription and splicing process, and compare and contrast two biologically plausible hypotheses about transcription rate variation. One assumes variation is due to DNA experiencing mechanical strain, while the other assumes it is due to regulator number fluctuations. We introduce a framework for numerically and analytically studying such models, and apply Bayesian model selection to identify candidate genes that show signatures of each model in single-cell transcriptomic data from mouse glutamatergic neurons.

Process Optimization for Up-Facing Surface Finish of AlSi10Mg Alloy Produced by Laser Powder Bed Fusion

This work investigates the effects of various processing parameters (laser power, scanning speed, hatch distance and beam offset) on the resultant inclined up-facing surface roughness of AlSi10Mg alloys produced by laser powder bed fusion (LPBF). A two-step approach, orthogonal test followed by the Doehlert matrix design (DMD) test is used to efficiently optimize the up-facing surface and contour parameters. The former method aims to determine the significance of variables while the latter one facilitates a rapid optimization. The results show that the interaction and interdependency among the parameters are of great significance to the obtainable surface roughness. Using a rational design of experiments, the optimized up-facing surface roughness of Ra of 5.4 μm is achieved. This is attributed to the elimination of the laser partition track and the reduction in irregularities at the edges of the parts. This work demonstrates an effective approach of experimental processing parameter optimization to improve the surface finish of LPBF parts.

Rational Design of a Polyurethane Foam.

Polyurethane (PU) foams are exceptionally versatile due to the nature of PU bond formation and the large variety of polymeric backbones and formulation components such as catalysts and surfactants. This versatility introduces a challenge, namely a near unlimited number of variables for formulating foams. In addition to this, PU foam development requires expert knowledge, not only in polyurethane chemistry but also in the art of evaluating the resulting foams. In this work, we demonstrate that a rational experimental design framework in conjunction with a design of experiments (DoE) approach reduces both the number of experiments required to understand the formulation space and reduces the need for tacit knowledge from a PU expert. We focus on an in-depth example where a catalyst and two surfactants of a known formulation are set as factors and foam physical properties are set as responses. An iterative DoE approach is used to generate a set of foams with substantially different cell morphology and hydrodynamic behaviour. We demonstrate that with 23 screening formulations and 16 final formulations, foam physical properties can be modelled from catalyst and surfactant loadings. This approach also allows for the exploration of relationships between the cell morphology of PU foam and its hydrodynamic behaviour.

Designing a Model-Driven Approach Towards Rational Experimental Design in Bioprocess Optimization.

To enable a more rational optimization approach to drive the transition from lab-scale to large industrial bioprocesses, a systematic framework coupling both experimental design and integrated modeling was established to guide the workflow executed from small flask scale to bioreactor scale. The integrated model relies on the coupling of biotic cell factory kinetics to the abiotic bioreactor hydrodynamics to offer a rational means for an in-depth understanding of two-way spatiotemporal interactions between cell behaviors and environmental variations. This model could serve as a promising tool to inform experimental work with reduced efforts via full-factorial in silico predictions. This chapter thus describes the general workflow involved in designing and applying this modeling approach to drive the experimental design towards rational bioprocess optimization.

Recursive Editing improves homology-directed repair through retargeting of undesired outcomes

CRISPR-Cas induced homology-directed repair (HDR) enables the installation of a broad range of precise genomic modifications from an exogenous donor template. However, applications of HDR in human cells are often hampered by poor efficiency, stemming from a preference for error-prone end joining pathways that yield short insertions and deletions. Here, we describe Recursive Editing, an HDR improvement strategy that selectively retargets undesired indel outcomes to create additional opportunities to produce the desired HDR allele. We introduce a software tool, named REtarget, that enables the rational design of Recursive Editing experiments. Using REtarget-designed guide RNAs in single editing reactions, Recursive Editing can simultaneously boost HDR efficiencies and reduce undesired indels. We also harness REtarget to generate databases for particularly effective Recursive Editing sites across the genome, to endogenously tag proteins, and to target pathogenic mutations. Recursive Editing constitutes an easy-to-use approach without potentially deleterious cell manipulations and little added experimental burden.

Challenge Test as Special Tool to Estimate the Dynamic of Listeria monocytogenes and Other Foodborne Pathogens

Over 23 million cases of foodborne disease (FBD) occur in Europe each year, with over 4700 deaths. Outbreaks of FBD have a significant impact on our society due to the high economic losses they cause (hospital treatment of affected patients and destruction of contaminated food). Among its health objectives, the European Union has set itself the goal of reducing the incidence of the main FBDs, approving various regulations that codify requirements in order to produce food that is “safe” for human consumption. Among these rules, Regulation 2005/2073 establishes precise food safety criteria for foods that are judged to be most at risk of causing episodes of FBD. The food business operator (FBO) must know their food better and know how to estimate whether a food can support the growth of food pathogens or if they are able to hinder it during the food’s shelf life. It is becoming crucial for each FBO to schedule specific laboratory tests (challenge tests) to establish the growth potential of individual pathogens and their maximum growth rate. In 2008 the European Union published the guidelines for programming the challenge tests for Listeria monocytogenes in RTE foods. These guidelines were further implemented in 2014 and again in 2019. In June 2019 the UNI EN ISO 20976-1 was published, which contains indications for setting up and carrying out challenge tests for all foodborne pathogens in all foods. In this article, we compare the three official documents to highlight their common aspects and differences, highlighting the advantages and disadvantages that each of them offers for those who have to set up a challenge test for the various foodborne pathogens. Our conclusion is that the challenge test is today the most effective tool to estimate the dynamics and growth potential of pathogenic microorganisms in food, if it is designed and implemented in a scrupulous way. It is important to develop a rational experimental design for each challenge test, and for each food, and this requires professionals who are experts in this specific field of study and who must be properly trained.

SELECTION AND JUSTIFICATION OF THE MATHEMATICAL 6 FACTORS EXPERIMENT PLAN IN THE STUDY OF ICE STARTING QUALITIES

The practical significance of the application of multivariate regression analysis in engineering practice as a necessary step towards improving and optimizing complex systems and processes is presented. The goal and investigation tasks have been actualized to optimize the start-up process of a high-speed subcompact diesel engine. Preparation of the plural factors experiment features of diesel start-up process based on regression analysis and the theory of mathematical design of experiment are presented. Qualitative optimizing parameters of the diesel engine cold start are determined: instantaneous acceleration of the crankshaft dn / dτ at the time of crankshaft cranking by an external energy source close to the time spent on starting the engine with optimal parameters of the starting system and starting energy consumed during the crankshaft cranking by an external energy source. Six main factors influence on certain parameters of the diesel engine start quality have been investigated. The regression equation for assessing the quality of the start-up process is substantiated. The form of regression equation is a full quadratic polynomial for the reproduction of the investigated functions, based on the previous study of some used in the study individual factors influence. The analysis of proposals for rational plan selection of a six-factor experiment to find the coefficients of the second-degree regression polynomial is carried out. The substantiation of the decision regarding the choice of the plan is given. Selection was based on the requirements of the investigation test bench and the conditions for study organizing. Main impact had the predecessors experience of similar investigation and the statistical criteria for different plans of experiment comparison used in mathematical theory of design of experiments. The points of the plan have been defined on a multidimensional cube due to the need to vary on three levels with a uniform step of all 6 factors that were identified as influential. Under the conditions of the available laboratory test bench, the current values of the factors of equivalent cold start temperature, crankshaft-cranking speed and maximum temperature of the glow plugs have a variance of the installation at different points of the plan, and the nature of the factors on energy consumption is unknown in advance. As a rational experimental design for organizing the current study, the Box-Wilson orthogonal central compositional plan was chosen, built by adding plan points on the axes of the factorial space to the full-factorial plan of the lowest order, while maintaining the requirement of orthogonality and symmetry of the plan.

Genomic Effects of Arginine on Cell Behavior of Conceptus Trophectoderm in Ungulates

Abstract Hatched ungulate (e.g., pigs, sheep and other ruminants) blastocysts undergo dramatic morphological transitions from spherical to tubular to filamentous forms to conceptuses (embryo/fetus and associated extraembryonic membranes) before implantation. L-Arginine (Arg), a conditionally essential amino acid, is required for this process to activate the mTOR cell signaling pathway to induce proliferation of both porcine and ovine conceptus trophectoderm cells. However, the genomic effects of arginine on trophectoderm cells is unknown. RNA-seq was used for a comparative transcriptome analysis of porcine and ovine trophectoderm cells to further understand effects of Arg on regulation of metabolism in trophectoderm cells. An established porcine trophectoderm (pTr) cell line isolated from D12 porcine conceptuses, as well as an established ovine trophectoderm (oTr) cell line isolated from D15 ovine conceptuses were used to determine response to Arg at the physiological concentration of 0.2 mM in a 48-h culture. In pTr cells, a total of 2,723 differentially expressed genes (DEG; 1,482 up and 1,241 down) were identified in response to Arg. In oTr cells, a total of 5,369 DEG (2,819 up and 2,550 down) were detected. Comparison analyses showed that the Arg-treated pTr and oTr transcriptomes share 873 common DEG (273 up and 342 down). Canonical pathway analyses identified the top enriched pathways in both pTr and oTr cells, including activation of actin cytoskeleton signaling, adrenomedullin signaling, and IGF-1 signaling; and inhibition of cell cycle G2/M checkpoint regulation, and p53 signaling. In response to Arg, pathways associated with cholesterol biosynthesis, and estrogen-mediated S-phase entry were exclusively activated in the pTr cells; whereas interferon signaling, ephrin receptor signaling, and integrin signaling were specifically activated in the oTr cells. Results from this study advance understanding of mechanisms responsible for elongation of ovine and porcine conceptuses and enable the rational design of future experiments.

Role of lncRNAs in the Development of Ischemic Stroke and Their Therapeutic Potential.

Stroke is a major cause of premature mortality and disability around the world. Therefore, identification of cellular and molecular processes implicated in the pathogenesis and progression of ischemic stroke has become a priority. Long non-coding RNAs (lncRNAs) are emerging as significant players in the pathophysiology of cerebral ischemia. They are involved in different signalling pathways of cellular processes like cell apoptosis, autophagy, angiogenesis, inflammation, and cell death, impacting the progression of cerebral damage. Exploring the functions of these lncRNAs and their mechanism of action may help in the development of promising treatment strategies. In this review, the current knowledge of lncRNAs in ischemic stroke, focusing on the mechanism by which they cause cellular apoptosis, inflammation, and microglial activation, has been summarized. Very few lncRNAs have been functionally annotated. Therefore, the therapies based on lncRNAs still face many hurdles since the potential targets are likely to increase with the identification of new ones. Majority of experiments involving the identification and function of lncRNAs have been carried out in animal models, and the role of lncRNAs in human stroke presents a challenge. However, mitigating these issues through more rational experimental design might lead to the development of lncRNA-based stroke therapies to treat ischemic stroke.

Assembled molecular beacon-based self-propelled DNA machine for enzyme-free and distinctly amplified nucleic acid detection

Abstract Sensitive nucleic acid detection by simple, rapid, cost-effective and enzyme-free amplified strategy is continually pursued for its extensive applications in bioanalysis and clinical disease diagnosis. Herein, an assembled molecular beacon-based self-propelled DNA machine was proposed for enzyme-free and distinctly amplified nucleic acid detection. The sensing system was consisted of three different DNA subunits and an assembled molecular beacon (aMB). Owing to the facile and programmable signal output of aMB, current strategy demonstrated two-level signal amplification capability for target detection: 1) target-triggered assembly unit could circularly dissociate aMB for the first-level signal amplification; 2) the liberated DNA sequence from the aMB could work as target analogy, contributing to the cascade and self-propelled signal amplification toward target DNA. The whole sensing system was well characterized by rational design of control experiments. The advantages of such sensing system also include assay simplicity (one-step mixing process) and no participation of protein enzyme. An attractive detection performance toward nucleic acid target (low detection limit of 10 fM, linear range of 0.1 pM-0.1 nM) could be achieved. It was also suitable for nucleic acid analysis in the relatively complicated biological matrix such as serum. Thus, current assembled molecular beacon-based DNA machine might be explored to develop various sensing systems for the potential applications in bioanalysis and clinical disease diagnosis.

Photochemical origin of reactive radicals and halogenated organic substances in natural waters: A review

Halogenated organic compounds, also termed organohalogens, were initially regarded to be of almost exclusively anthropogenic origin. However, recent research has demonstrated that photochemical reactions are important abiotic sources of organohalogen compounds in sunlit surface waters. Halide ions (X−, X represents Cl, Br and I) are common anions in natural waters and might be oxidized by reactive species originated from photochemistry of dissolved organic matter (DOM) or inorganic photoactive species. The resulting reactive halogen species may react with organic substances with diverse bimolecular reaction rate constants, depending on the complexity and structure of organic substances. Therefore, the chemical mechanism of halogenation remains challenging to be fully elucidated. To better understand the trends in the existing data and to identify the knowledge gaps that may merit further investigation, this review gives an integrative summary on the sources of reactive oxygen species (ROS) and halogen radicals (X/X2−). Photochemical halogenation of phenolic compounds and formation of methyl halide and brominated organic pollutants are highlighted. By evaluating existing literature and identifying some uncertainties, this review emphasizes the environmental significance of sunlight-driven halogenation and proposes further research directions on mechanistic investigation and rational experimental design close to natural systems.

Chemours sues DuPont, its former parent, but the case is dismissed

Formulators do not naturally turn to statistical thermodynamics for experimental inspiration. However, with the newer, intuitive approach to statistical thermodynamics, the formulator gains deep insights into the hitherto confusing effects of ‘cosolvents’, ‘hydrotropes’, ‘solubilizers’ that affect properties such as solubility, gelation or conformational stability. The historical confusion has arisen from classical approaches that simply cannot disentangle causes and effects. The aim of this review is to demonstrate how a formulator can work with statistical thermodynamics towards a rational design of experiments and an unambiguous interpretation of the driving forces behind cosolvent effects.

PPDiffuse: A Quantitative Prediction Tool for Diffusion of Charged Polymers in a Nanopore.

Nanopore-based sensing of charged biopolymers is a powerful single-molecule method. In aconventional nanopore experiment, a single biological (proteinaceous) or solid-state nanopore perforates a thin membrane that is wetted by, and electrically isolates, two opposing reservoirs of electrolyte solution. A potential is applied across the membrane via external electronics coupled to the electrolyte reservoirs with electrochemical electrodes, actuating the system. The electric field set up by the applied potential in the nanopore and its immediate environment plays two roles: supporting an ionic current through the nanopore, which reports on the properties of the pore and its contents; and acting on analyte molecules to attract them to, and drive them into, the nanopore. The presence of a large biopolymer in the pore modulates the ionic current 𝐼(𝑡). The duration of the ionic current modulation corresponds to the length of time the polymer spends in the pore from capture to its ultimate escape, either by retraction to the reservoir from which it was captured, or by translocation to the opposite reservoir . The probabilities of retraction or translocation, or splitting probabilities, and the corresponding distributions of escape times (𝑡esc), are particularly sensitive to the size and charge of the analyte molecule and have been the focus of much theoretical, computational, and experimental effort. An underlying physical framework in which the distribution of escape times is modeled as a first-passage time from a one-dimensional potential is quantitatively predictive for a wide range of experiments. The complexity of this potential for the general case, however, requires calculations to guide experimental design that can be tedious to implement. PPDiffuse is intended to remove this burden from the nanopore research community and enable convenient, rational design of nanopore experiments with complex substrates such as polypeptides.

Influence of Genetic Background and Sex on Gene Expression in the Mouse (Mus musculus) Tail in a Model of Intervertebral Disc Injury.

To facilitate rational experimental design and fulfill the NIH requirement of including sex as a biologic variable, we examined the influences of genetic background and sex on responses to intervertebral disc (IVD) injury in the mouse tail. The goal of this study was to compare gene expression and histologic changes in response to a tail IVD injury (needle puncture) in male and female mice on the DBA and C57BL/6 (B6) backgrounds. We hypothesized that extracellular matrix gene expression in response to IVD injury differs between mice of different genetic backgrounds and sex. Consistent changes were detected in gene expression and histologic features after IVD injury in mice on both genetic backgrounds and sexes. In particular, expression of col1a1 and adam8 was higher in the injured IVD of DBA mice than B6 mice. Conversely, col2a1 expression was higher in B6 mice than DBA mice. Sex-associated differences were significant only in B6 mice, in which col2a1 expression was greater in male mice than in female. Histologic differences in response to injury were not apparent between DBA and B6 mice or between males and females. In conclusion, mouse tail IVD showed sex- and strain-related changes in gene expression and histology after needle puncture. The magnitude of change in gene expression differed with regard to genetic background and, to a lesser degree, sex.

A systematic investigation of key factors of nucleic acid precipitation toward optimized DNA/RNA isolation.

Nucleic acid precipitation is important for virtually all molecular biology investigations. However, despite its crucial role, a systematic study of the influence factors of nucleic acid precipitation has not been reported. In the present work, via rational experimental design, key factors of nucleic acid precipitation, including the type of nucleic acid, temperature and time of incubation, speed and time of centrifugation, volume ratio of ethanol/isopropanol to nucleic acid solution, type of cation-containing salt solution and type of coprecipitator, were comprehensively evaluated in an attempt to maximize the efficiency of nucleic acid precipitation. Our results indicate that the optimal conditions of each influence factor of nucleic acid precipitation may vary in accordance with the chemistry, structure and length of nucleic acids.

Investigating the Catalytic Active Sites of Mo/HZSM-5 and Their Deactivation During Methane Dehydroaromatization

Molybdenum supported on zeolite HZSM-5 (Mo/HZSM-5) is the most studied catalyst for methane dehydroaromatization (MDA). However, the nature of its catalytic active sites and their deactivation mechanisms remain unclear and controversial. Here we report new insights into this system, on the basis of advanced characterization and a rational design of experiments. We find that it is the size of the HZSM-5 crystal that determines the form and location of the catalytic active molybdenum carbide (MoCx) species, and thus the performance of Mo/HZSM-5; we also find that MoCx sites are preferentially deactivated over acid sites, when supported on nano-sized HZSM-5. These findings lead us to develop an “encapsulation” strategy, which effectively reconciles the deactivation rates at the MoCx sites and the acid sites, enabling a full utilization of both sites, and consequently leading to a 10-fold increase in catalyst lifetime and aromatics yield. Our results indicate that MoCx particles formed outside the micropores of HZSM-5, which are traditionally considered detrimental to the reaction, can serve as active sites for MDA, provided that they are properly protected from direct exposure to coke deposition. These findings allow us to design control experiments to answer an open question whether the acid sites, in addition to promoting the dispersion of Mo species, play a catalytic role in the MDA reaction, and the results show that acid sites are indeed essential for the conversion of methane.