Experimental Research Research Articles

Experimental research on mechanically and thermally activation of nano-kaolin to improve the properties of ultra-high-performance fiber-reinforced concrete

Abstract The rising demand for ultra-high-performance concrete (UHPC) necessitates innovations in sustainable materials. This study explores the substitution of ordinary Portland cement (OPC) with thermally and mechanically activated nano-kaolin in varying proportions from 0.5 to 0.25%. A uniform quantity of double-hooked end steel fibers was added to all the mixes. Activated nano-kaolin variants showed significant enhancement in UHPC properties. Specifically, UHPC with 0.20% thermally activated kaolin (B3-TAK-20) exhibited a 21.6% increase in compressive strength and a 25.5% increase in modulus of elasticity at 90 days, with the modulus of rupture doubling compared to the reference mix. These improvements are attributed to the amorphous nature of thermally activated nano-kaolin, resulting in a denser concrete matrix and reduced porosity. Beyond the optimal 0.20% kaolin replacement, an increase to 0.25% diminished compressive strength. Durability tests showed enhanced acid resistance, with only a 6.7% mass loss for the thermally activated nano-kaolin mix and a consistent reduction in water absorption by 14.4% as kaolin proportions increased from 0.5 to 0.25%. The study also noted a decrease in water absorption by 22.9 and 12.3% at 56 and 90 days, respectively, indicating the thermally activated nano-kaolin’s enhanced performance. This research underscores the potential of activated kaolin as a viable alternative to OPC, paving the way for more sustainable UHPC production.

Exploring application of neuromarketing in advertising effectiveness: a bibliometric and TCCM-based approach

PurposeThe study was devise to summarizees the underlying theories and concepts in the context of probing effective advertising through neuromarketing.Design/methodology/approachThe given review has applied a bibliometric analysis to conduct performance and science mapping. We adopted Theory, Context, Character and Methodology (TCCM) approach for conducting systematic literature review.FindingsThe study found that emotion theory was mostly implemented for the related research. The researchers used emotion induced stimuli for measuring responses preferably through electroencephalogram (EEG), galvanic skin response (GSR) and eye tracking (ET). Experimental studies provided more insightful results, despite conducting research on few participants.Research limitations/implicationsFuture researchers are advised to perform interdisciplinary research pertaining to the neuroscience and psychology.Originality/valueThe paper provides a thorough understanding of the theories, constructs and methodologies appropriate for conducting experimental neuromarketing research. The existing review papers lack to perform review on TCCM approach in the context of neuromarketing in advertising.

Algorithm for monitoring water quality parameters in optical systems based on artificial intelligence data mining.

Due to the increasingly serious water environment pollution, the difficulty of Water Quality Monitoring (abbreviated as WQM for convenience) is also constantly increasing, which puts forward more requirements for the capabilities of various aspects of WQM systems. However, the current WQM method has drawbacks such as slow speed, long monitoring time, complex operation, poor stability, and the inability to obtain accurate information on water pollution in the first time, as well as the generation of toxic and harmful secondary pollutants after some measurement parameters are tested. To address these issues and ensure water quality safety, this paper investigated the algorithm for monitoring water quality parameters using artificial intelligence data mining optical systems. This article applied an artificial intelligence data mining system to detect water quality and designed various system through this method to improve system performance. To verify the actual effectiveness of artificial intelligence data mining systems, this article selected 10 water plants as experimental research subjects and compared the differences between traditional WQM methods and WQM methods based on artificial intelligence data mining systems in terms of WQM time, accuracy, sensitivity, and protective performance. The experimental results showed that the optical system based on artificial intelligence data mining took an average of 2.7 days in WQM and the average accuracy was 85.95%. The average sensitivity value was 84.19% and the average protective score was 8.46 points. This indicated that artificial intelligence data mining optical technology had vital significance and value for WQM.

Analysis of Land Surface Performance Differences and Uncertainty in Multiple Versions of MODIS LST Products

Moderate Resolution Imaging Spectroradiometer (MODIS) Land Surface Temperature (LST) products are essential data sources for global and regional climate change research. Currently, several versions of the MODIS LST product have been released, yet the performance differences and uncertainties they introduce in land surface studies remain insufficiently addressed. To bridge this gap, this study focuses on four distinct versions of the LST product: MxD11A1 Collection 5 (C5), Collection 6 (C6), Collection 6.1 (C6.1), and MxD21A1 Collection 6.1 (MxD21). The spatial resolution of all product generations is 1 km, and the temporal resolution is 0.5 days. This study provides a comprehensive analysis of the errors arising from different generations of these products in various land surface process studies. The error assessment includes cross-comparisons between product versions and evaluations of the absolute errors generated. Absolute errors in evaluation data were collected from 13 surface sites within the Heihe Watershed Allied Telemetry Experimental Research (HiWATER) project during the period 2013–2018. Cross-validation results show that the largest difference between C5 and C6.1 occurs over bare land, with an RMSE of approximately 1.45 K, while there is no significant change between C6 and C6.1. MOD21 shows considerable variation compared to C6.1 at night across different land cover types, with RMSE over cropland exceeding 2 K. The temperature difference between MOD21 and C6.1 is more pronounced at night (2.01 K) than during the day (0.30 K). Validation results based on temperature indicate that C5 has greater uncertainty compared to C6, especially over bare land, where errors are 2.06 K and 1.06 K, respectively. Furthermore, MxD21 demonstrates significant day–night performance discrepancies, with an average bias of 0.10 K at night, while daytime errors over bare land can reach 2 K, potentially influenced by atmospheric conditions. Based on the research in this paper, it is possible to clarify the performance of different versions of MODIS products, reflecting the appropriateness of their past applications; on the other hand, it is recommended to prioritize the use of the MxD11A1 C6 and C6.1 products for monitoring and applications in bare soil areas to ensure higher accuracy. Furthermore, for day and night monitoring, it may be beneficial to alternate between the MxD11A1 and MxD21A1 products to fully leverage their respective advantages and enhance overall monitoring effectiveness.

Unambiguous discrimination of general quantum operations.

The discrimination of quantum operations has long been an intriguing challenge, with theoretical research notably advancing our understanding of the quantum features in discriminating quantum objects. This challenge is closely related to the discrimination of quantum states, and proof-of-principle demonstrations of the latter have already been realized using optical photons. However, the experimental demonstration of discriminating general quantum operations, including both unitary and nonunitary operations, has remained elusive. In general quantum systems, especially those with high dimensions, the preparation of arbitrary quantum states and the implementation of arbitrary quantum operations and generalized measurements are nontrivial tasks. Here, we experimentally demonstrate the optimal unambiguous discrimination of up to six displacement operators and the unambiguous discrimination of nonunitary quantum operations. Our results demonstrate powerful tools for experimental research in quantum information processing and are expected to stimulate a wide range of valuable applications in the field of quantum sensing.

In vivo biodegradation rates of domestic membranes for guided tissue regeneration

Background: Experimental research is required to provide guidelines for the use of domestic biodegradable membranes. Aim: To assess the swelling index and biodegradation rate of barrier membranes (non-collagen and collagen) for guided tissue regeneration in vitro, as well as their biocompatibility in vivo. Materials and Methods: The swelling index of two types of membranes was assessed after 24 h of exposure to phosphate-buffered saline (PBS) at different pH levels (6.50 and 7.37). The spontaneous degradation rate of the two types of membranes was assessed; changes in their weight following exposure to PBS at different pH levels (6.50 and 7.37) were measured at predetermined time points. Moreover, the biocompatibility of two membrane samples following subcutaneous implantation in B/D male mice was assessed. Results: The swelling index of non-collagen membranes was higher at neutral pH compared to acidic pH: 7.7 for pH 7.37 vs 7.2 for pH 6.50. For collagen membranes, the swelling index was pH-independent. There were no differences in membrane weight loss following exposure to PBS at pH 6.5 during 8 weeks. During the first two weeks, collagen membranes had a higher resorption rate at pH 7.37 than non-collagen membranes. Following subcutaneous implantation of both membranes, histopathological specimens collected two weeks after surgery revealed the formation of foreign body granulomas with well-defined boundaries around the implants. Macrophages, monocytes, single giant cells of foreign bodies, and Pirogov–Langhans giant cells were detected, with the number gradually increasing over time. Conclusion: Non-collagen membranes had a larger swelling index than collagen membranes, which depended on pH. At pH 7.37, collagen membranes had a higher resorption rate during the first two weeks compared to non-collagen membranes. In vitro weight loss after 8 weeks was 20–30% for both membranes, regardless of pH. Subcutaneous implantation in mice confirmed the biocompatibility of the membranes. The biodegradation rate of non-collagen membranes was higher than that of collagen membranes.

Abstract B008: Exploring the Dual Roles of RNAs as Oncogenic Drivers and Therapeutic Targets in Cancer: A Cross-Sectional Study

Abstract RNAs have emerged as pivotal molecules in the complex landscape of cancer biology, acting as both drivers of oncogenesis and therapeutic targets. This cross-sectional experimental design research explores the dual roles of RNAs, particularly focusing on the interplay between non-coding RNAs (ncRNAs), messenger RNAs (mRNAs), and their regulatory networks in various cancer types. The study systematically analyzes RNA expression profiles across a broad spectrum of cancer samples, employing high-throughput sequencing technologies combined with bioinformatics tools to identify key RNA species that contribute to tumorigenesis. The experimental design includes the collection of tumor and adjacent normal tissues from patients diagnosed with breast, lung, and colorectal cancers. RNA extraction and sequencing are performed to generate comprehensive expression profiles. Differential expression analysis is conducted to pinpoint RNAs that are significantly dysregulated in cancerous tissues compared to their normal counterparts. Subsequently, functional assays, including RNA interference (RNAi) and CRISPR/Cas9-mediated gene editing, are utilized to validate the oncogenic or tumor-suppressive roles of these RNAs in cancer cell lines. To further elucidate the mechanistic pathways, the study integrates data from RNA-binding protein immunoprecipitation (RIP) and crosslinking immunoprecipitation (CLIP) assays, providing insights into the RNA-protein interactions that influence cancer progression. Additionally, the research evaluates the therapeutic potential of targeting these RNAs, using small interfering RNAs (siRNAs) and antisense oligonucleotides (ASOs) in preclinical cancer models. Preliminary findings highlight the significance of long non-coding RNAs (lncRNAs) and micro RNAs (miRNAs) in modulating key signaling pathways involved in cell proliferation, apoptosis, and metastasis. This study underscores the importance of RNA-based diagnostics and therapeutics, paving the way for innovative treatment strategies in cancer management. Citation Format: Fathelrahman M Gameel. Exploring the Dual Roles of RNAs as Oncogenic Drivers and Therapeutic Targets in Cancer: A Cross-Sectional Study [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: RNAs as Drivers, Targets, and Therapeutics in Cancer; 2024 Nov 14-17; Bellevue, Washington. Philadelphia (PA): AACR; Mol Cancer Ther 2024;23(11_Suppl):Abstract nr B008.

Polyurethane foam adhesive with fly ash as a sustainable stabilizer

ABSTRACT This experimental research work investigates the role of polyurethane foam adhesive (PFA) in evaluating the unconfined compressive strength (UCS) of three different types of sands with varying maximum particle sizes (Dmax = 4.00 mm, 2.00 mm, and 0.63 mm) mixed with different proportions of fly ash (FA = 0%, 5%, 10%, and 15%). Moreover, this study includes the influence of the polymer percentages (PFc = 2%, 4%, 6%, and 8%) and particle size characteristics on the UCS of the tested materials. The obtained results indicate a significant increase in the UCS and ductility at a particular fly ash content (FA = 5%), where the increment of the polyurethane foam content from PFc = 2% to 8% induces an increasing of the UCS from 1.64 MPa to 5.28 MPa (a 3.2% increase). It is found that higher values of PFc = 8% and FA = 15% exhibit noticeable improvements and stabilization of the strength properties in the tested mixtures. Additionally, the UCS of the enhanced mixtures can be predicted using a newly introduced parameter called the polymer factor index (IPF), which considers the porosity and PFA content properties. The PFA-enhanced sand-fly ash mixtures offer a promising and innovative solution in many geotechnical engineering applications due to their reliable mechanical performances.

THE EFFECT OF USING BIG BOOK MEDIA ON THE READING ABILITY OF GRADE II STUDENTS OF SDN 68 PALEMBANG

The effect of using big book media on the reading ability of class II students at SDN 68 Palembang aims to determine the effect of using big book media on students' valid and practical reading ability. The type of research used is quantitative. with this type of Quasi Experimental research using saturated sampling techniques. Based on the results of data analysis and research discussion, it can be concluded that there is a significant influence on the use of big book media on students' reading ability in class II of SD Negeri 68 Palembang. Based on the results of research that has been carried out, it is stated that there are differences in Indonesian language learning outcomes between classes that were given treatment and those that were not given treatment, which can be seen from the average score in the experimental class of 85.57, while the average score of the control class was 77.62. To find out the comparison between the two classes, an Independent T Test was carried out, which sig (2 tailed) 0.000 < 0.05 or and control class

Assessment of the impact of intensification of heat exchange processes in heaters on power unit efficiency

In various spheres of modern industry, including power engineering, the issue of low efficiency of operation of heatexchange equipment, where condensation of vapour-gas media occurs, is a significant concern. It is known that one of the effective ways of solving this problem is intensification of heat exchange processes by transitioning from the traditional film condensation mode to the droplet condensation mode. A promising and technically straightforward way to achieve the droplet mode of condensation is a method based on the treatment of heat-exchange surfaces using a surfactant – octadecylamine (ODA). The analysis of studies, in which results of experimental research and industrial implementation are given, has shown that application of the specified method in conditions of condensation of water steam promotes formation of a stable droplet mode of condensation; as a result, the heat transfer coefficient on the steam side can more than double.Equally important and interesting is the question of how the efficiency of heat exchangers impacts the overall efficiency of complex systems, in which they operate. In this study, we examined the effect of the operational efficiency of network and regenerative heaters on the energy efficiency and economic performance of a power unit based on steam turbine unit T-110-12,8-3 operating in the heat recovery mode. To this end, we calculated the thermal scheme under various operating conditions of these heat exchangers and determined several key efficiency indicators, including the fuel heat utilisation factor (HUF) and electrical efficiency (EE). It is revealed that intensification of heat-exchange processes by a factor of 2 in horizontal delivery water heaters (HDWH) or in low-pressure heaters (LPH) significantly enhances the efficiency indicators of the power unit as a whole. At the same time, as the analysis of calculation results showed, the greatest benefits are realized when this measure is implemented in HDWH.

Enhanced Optoelectronic Properties of Few-Layer Graphene for Transparent Conducting Electrode: Density Functional Theory Perspective

The challenge in utilizing graphene for transparent conducting electrodes (TCE) is to increase the electronic conductivity without significantly decreasing its transmittance. Forming a few-layer structure is an effective way to modify the electronic properties of graphene. In this paper, we used the density functional theory (DFT) method to study the effect of the number of few-layer graphene (FLG) layers on the electronic conductivity and transmittance. We find that increasing the number of layers leads to an increase in electronic conductivity. On the other hand, a decrease in transmittance was observed when the number of layers was increased. Based on the performance analysis, the optoelectronic properties of FLG with more than three layers were better than those of conventional TCE materials, such as indium tin oxide (ITO) and aluminium-doped zinc oxide (AZO). The calculated electronic conductivity and transmittance of a 4-layer FLG were 2.23×1019 (1/Ω.m.s) and 95.69%, respectively. This finding can be used as guidance for experimental research in developing graphene-based TCE materials. Keywords: density functional theory, electronic conductivity, few-layer graphene, transmittance

The effect of REACT model implementation on learning outcomes and critical thinking skills of students of SMAN 9 KENDARI

Quality human resources will be formed if education focuses on improving students’ skills. Of course, the foundation of education must be quality. Qualified human resources will later be responsible for making Indonesia a good country in all fields. This study aims to examine the effect of applying the REACT learning model (Relating, Experiencing, Applying, Cooperating, Transferring) on learning outcomes and critical thinking skills of students of SMAN 9 KENDARI. Quantitative research method with experimental research type. The research design used was post experimental control design. The research location was at SMAN 9 KENDARI. The instruments used include learning outcomes test and critical thinking skills test. The data obtained were explained using statistical tests to see the differences between the experimental group and the control group in chemistry subjects. The results showed that the application of REACT model significantly improved students’ learning outcomes and critical thinking skills compared to conventional learning methods in chemistry subjects. The findings indicated that the REACT model was effective in improving the quality of learning and developing critical thinking skills of students of SMAN 9 KENDARI, especially in chemistry learning.

Experimental Study on the Pelleting and Coating Performance of Red Clover Seeds

This study aimed to optimize the pelleting and coating process for red clover seeds, addressing the issue of low pelleting success rates. Through theoretical analysis and experimental research, coating pan fill rate, powder supply quantity, and pelleting time were identified as key factors influencing the pelleting success rate. Single-factor experiments were conducted to investigate the effects of these parameters on the quality of red clover seed pelleting and coating. Based on these results, orthogonal trials were carried out, and response surface analysis was employed to reveal the influence patterns and interactions of each factor. The research results indicate that the factors affecting the pelleting success rate, ranked in order of importance, are coating pan fill rate, pelleting time, and powder supply quantity. Through mathematical model optimization, the optimal combination of process parameters was determined to be coating pan fill rate of 35.9%, powder supply quantity of 160.2 g, and pelleting time of 6.9 s. Under these conditions, a pelleting success rate of 94.3% was achieved in validation experiments. This study provides a theoretical foundation and practical guidance for optimizing the pelleting and coating process of red clover seeds, which is significant for improving seed coating quality and promoting red clover cultivation.

A magnetic pendulum for demonstrating Mathieu-type parametric resonance

Abstract We describe a magnetic pendulum apparatus designed for studying and demonstrating Mathieu-type parametric resonance. The apparatus features a T-shaped rigid arm with a neodymium (NdFeB) magnet bob, suspended by double V-shaped thread suspensions. It is parametrically driven by an alternating uniform magnetic field produced by Helmholtz coils. The pendulum is structurally simple and easy to implement, and its parameter-driven mechanism is transparent in terms of physics. The use of electromagnetic driving allows for precise control over both the frequency and amplitude of parameter modulation, enabling quantitative studying on parametric resonance. We have observed the first and second instability region of parametric resonance. We also theoretically analyze the impact of non-uniformity in the Helmholtz coils' magnetic field on the pendulum motion, revealing that the non-uniformity can significantly alter the pendulum's nonlinearity, influencing both the magnitude and the sign of the coefficients of the nonlinear terms. However, under the parameters of our experimental setup, the nonlinearity effect is mainly manifested in large swing angle motions. For small swing angle within 5\textsuperscript{$\circ$}, the impact of magnetic field non-uniformity on parametric resonance is negligible. The experiment reported here is appropriate for undergraduates to carry out experimental research, helping them develop a more intuitive and quantitative understanding of parametric resonance.

Boolean network modeling and its integration with experimental read-outs : An interdisciplinary presentation using a leukemia model.

The limited availability of suitable animal models and cell lines often impedes experimental cancer research. Wet-laboratory experiments are also time-consuming and cost-intensive. In this review, we present an in silico modeling strategy, namely, Boolean network (BN) models, and demonstrate how it could be applied to streamline experimental design and to focus the effort of experimental read-outs. Boolean network models allow for the dynamic analysis of large molecular signaling pathways and their crosstalks. After establishing and validating aspecific tumor model, mechanistic insights into the tumor cell behavior can be gained by studying the trajectories of different tumor phenotypes. Also, tumor driver and drug target screenings can be performed. These automatic screenings can help to identify new intervention targets and putative biomarkers for tumor evolution, hence guiding new wet-laboratory experiments. The goal of this round-up is to demonstrate how to establish, validate, and use BN modeling and its crosstalks in classic wet-laboratory research using achronic lymphocytic leukemia (CLL) BN model.

Computational Studies of Molybdenum-Containing Metal–Sulfur and Metal–Hydride Clusters

The development of transition metal clusters is an active area of research in inorganic chemistry, as they can be used as catalysts to perform chemically or biologically relevant reactions. Computational chemistry, employing density functional theory (DFT), plays a key role in rationalizing the electronic structure and properties of transition metal clusters. This article reviews recent quantum chemical studies of Mo3S4M clusters (M = Fe, Co, Ni), their CO- or N2-bound variants, and metal–hydride clusters. The ground state of the cluster systems was computed, and properties such as metal–metal bonding, orbital interactions, fluxional behavior of ligands, spectroscopy, and reaction mechanisms were rationalized and compared with available experimental results. Our research findings evidence that computational studies employing quantum chemical methods can guide experimental researchers to develop novel transition metal clusters for potential applications in catalysis.

Enhanced electrorheological activity of rod-like Fe3O4@TiO2 particles in elastomers and their morphological effect

Abstract The properties of dielectric particles play the most important role in the electrorheological (ER) activity of elastomers, however, there is a lack of experimental research on the effect of particle morphology on the properties of electrorheological elastomers (EREs). In this study, spherical and rod-like Fe3O4@TiO2 core-shell particles were synthesized and used to prepare EREs. Particles characterization results showed that both spherical and rod-like Fe3O4@TiO2 exhibited an obvious core-shell structure and similar magnetic properties,with the aspect ratio of rod-like Fe3O4@TiO2 particles was approximately 7:1. Furthermore, the EREs filled with rod-like Fe3O4@TiO2 particles exhibit a higher dielectric constant and sharper dielectric loss peak than those filled with spherical particles, indicating that a larger aspect ratio enhances the dielectric performance. For EREs with the same volume fraction of particles or cured under the same external field, the storage modulus and relative ER effect of EREs filled with rod-like particles are both higher than those of EREs containing spherical particles. Additionally, it can be confirmed that the rod-like particle chain structures have a more significant strengthening effect on the ERE matrix, as evidenced by the rod-like Fe3O4@TiO2 particle filled elastomers exhibiting lower creep strain.

Revealing the potential therapeutic mechanism of Lonicerae Japonicae Flos in Alzheimer’s disease: a computational biology approach

BackgroundAlzheimer’s disease (AD) is a degenerative brain disease without a cure. Lonicerae Japonicae Flos (LJF), a traditional Chinese herbal medicine, possesses a neuroprotective effect, but its mechanisms for AD are not well understood. This study aimed to investigate potential targets and constituents of LJF against AD.MethodsNetwork pharmacology and bioinformatics analyses were performed to screen potential compounds and targets. Gene Expression Omnibus (GEO) datasets related to AD patients were used to screen core targets of differential expression. Gene expression profiling interactive analysis (GEPIA) was used to validate the correlation between core target genes and major causative genes of AD. The receiver operating characteristic (ROC) analysis was used to evaluate the predictive efficacy of core targets based on GEO datasets. Molecular docking and dynamics simulation were conducted to analyze the binding affinities of effective compounds with core targets.ResultsNetwork pharmacology analysis showed that 112 intersection targets were identified. Bioinformatics analysis displayed that 32 putative core targets were identified from 112 intersection targets. Only eight core targets were differentially expressed based on GEO datasets. Finally, six core targets of MAPK8, CTNNB1, NFKB1, EGFR, BCL2, and NFE2L2 were related to AD progression and had good predictive ability based on correlation and ROC analyses. Molecular docking and dynamics simulation analyses elucidated that the component of lignan interacted with EGFR, the component of β-carotene interacted with CTNNB1 and BCL2, the component of β-sitosterol interacted with BCL2, the component of hederagenin interacted with NFKB1, the component of berberine interacted with EGFR and BCL2, and the component of baicalein interacted with NFKB1, EGFR and BCL2.ConclusionThrough a comprehensive analysis, this study revealed that six core targets (MAPK8, CTNNB1, NFKB1, EGFR, BCL2, and NFE2L2) and six practical components (lignan, β-carotene, β-sitosterol, hederagenin, berberine, and baicalein) were involved in the mechanism of action of LJF against AD. Our work demonstrated that LJF effectively treats AD through its multi-component and multi-target properties. The findings of this study will establish a theoretical basis for the expanded application of LJF in AD treatment and, hopefully, can guide more advanced experimental research in the future.

Advances in machine learning methods in copper alloys: a review.

Advanced copper and copper alloys, as significant engineering structural materials, have recently been extensively used in energy, electron, transportation, and aviation domains. Higher requirements urge the emergence of high-performance copper alloys. However, the traditional trial-and-error experimental observations and computational simulation research used to design and develop novel materials are time-consuming and costly. With the accumulation of material research and rapid development of computational ability, the thorough application of material genome engineering has sped up the development of novel materials and facilitates the process of systematic engineering application. This review summarizes the benefits of data-driven machine learning techniques and the state of the art of machine learning research in the area of copper alloys. It also displays the widely used computational simulation approaches (e.g., the first-principles calculation, molecular dynamics simulation, phase-field simulations, and finite element analysis) and their combined applications in material design and property prediction. Finally, the limitations of machine learning research methods are outlined, and future development directions are proposed.

Synergistic Electronic Interaction in PdCu Alloy/TiO2-NSs for Ambient Efficient Dehydrogenation of Formic Acid.

Palladium-based catalysts are remarkable in endorsing hydrogen (H2) generation through formic acid (HCOOH, FA) dehydrogenation under near-ambient conditions. Hydrogen energy efficiency depends on high-performance catalyst design. In this study, Pd-Cu nanoalloy catalysts with mutable atomic ratios are successfully fabricated on TiO2 nanosheets (TiO2-NSs).The synergistic electronic interactions between Palladium (Pd) and copper (Cu) are revealed through a density of states (DOS) analysis of alloy supported over a mixed valence state of Ti linked to oxygen vacancies (Vo) in TiO2-NSs, Enhanced adsorption of target molecules reveals novel active sites for Formic acid dehydrogenation (FAD), with O─H bond cleavage via HCOO formate intermediate preceding C─H and C─O bond cleavage. Experimental and theoretical research demonstrates that the Pd-Cu/TiO2-NSs (3:7) catalyst d electron redistribution and d-band center shift lower the activation energy for O─H bond cleavage, exhibit superior H2 production than pure Pd and Cu, increasing Pd electron density due to synergistic effects from reactive crystal facets, defects, and strong metal support interactions (SMSI), lowering the activation energy for HCOOH dissociation step, generating carbon dioxide (CO2) and H2 with a unprecedented high turnover frequency (TOF) of 6268 molH2.h-1.molPd-1 at 303K with activation energy (Ea) of 15 KJ mol-1. This attempt models an efficient HCOOH-to-hydrogen catalyst.