Interfacial Modeling of Fibrinogen Adsorption onto LiNbO3 Single Crystal-Single Domain Surfaces.

For the development of next-generation protein-based biosensor surfaces, it is important to understand how functional proteins, such as fibrinogen (FBG), interact with polar substrate surfaces in order to prepare highly sensitive points of medical care diagnostics. FBG, which is a fibrous protein with an extracellular matrix, has both positively and negatively charged regions on its 3-dimensional surface, which makes interpreting how it effectively binds to polarized surfaces challenging. In this study, single-crystal LiNbO3 (LNO) substrates that have surface charges were used to investigate the adsorption of FBG protruding polar fragments on the positively and negatively charged LNO surfaces. We performed a combination of experiments and multi-scale molecular modeling to understand the binding of FBG in vacuum and water-solvated surfaces of LNO. XPS measurements showed that the FBG adsorption on LNO increased with increment in solution concentration on surfaces independent of charges. Multi-scale molecular modeling employing Quantum Mechanics, Monte Carlo, and Molecular Mechanics addressed the phenomenon of FBG fragment bonding on LNO surfaces. The binding simulation validated the experimental observation using zeta potential measurements which showed presence of solvated medium influenced the adsorption phenomenon due to the negative surface potential.

Lattice and Valence Electronic Structures of Crystalline Octahedral Molybdenum Halide Clusters-Based Compounds, Cs2[Mo6X14] (X = Cl, Br, I), Studied by Density Functional Theory Calculations.

The electronic and crystal structures of Cs2[Mo6X14] (X = Cl, Br, I) cluster-based compounds were investigated by density functional theory (DFT) simulations and experimental methods such as powder X-ray diffraction, ultraviolet-visible spectroscopy, and X-ray photoemission spectroscopy (XPS). The experimentally determined lattice parameters were in good agreement with theoretically optimized ones, indicating the usefulness of DFT calculations for the structural investigation of these clusters. The calculated band gaps of these compounds reproduced those experimentally determined by UV-vis reflectance within an error of a few tenths of an eV. Core-level XPS and effective charge analyses indicated bonding states of the halogens changed according to their sites. The XPS valence spectra were fairly well reproduced by simulations based on the projected electron density of states weighted with cross sections of Al Kα, suggesting that DFT calculations can predict the electronic properties of metal-cluster-based crystals with good accuracy.

Evidence that microplastics at environmentally relevant concentration and size interfere with energy metabolism of microalgal community.

To address two current issues in evaluating the toxicity of microplastics (MPs) namely, conflicting results due to species specificity and the ecological irrelevance of laboratory data, this study conducted a 10-day exposure experiment using a microalgal community comprising three symbiotic species. The experiment involved virgin and Benzo[a]pyrene-spiked micron-scale fibers and fragments made of polyethylene terephthalate (PET) and polypropylene (PP). The results showed that, from a physiological perspective, environmentally relevant concentrations of micron-scale MPs decreased saccharide accumulation in microalgal cells, as confirmed by ultrastructural observations. MPs may increase cellular energy consumption by obstructing cellular motility, interfering with nutrient uptake, and causing sustained oxidative stress. Additionally, MPs and adsorbed B[a]P induced DNA damage in microalgae, potentially further disrupting cellular energy metabolism. Ecologically, MPs altered the species abundance in microalgal communities, suggesting they could weaken the ecological functions of these communities as producers and affect ecosystem diversity and stability. This study marks a significant advancement from traditional single-species toxicity experiments to community-level assessments, providing essential insights for ecological risk assessment of microplastics and guiding future mechanistic studies utilizing multi-omics analysis.

Nitrogen Minimization in Hydrothermal Liquefaction Biocrude from Sewage Sludge with Green Extraction Solvents.

This study explored the effectiveness of hydrothermal liquefaction (HTL) in converting sewage sludge (SS) into high-quality biocrude. It scrutinized the influence of various solvents, including conventional choices like dichloromethane (DCM) and hexane, alongside environmentally friendly alternatives, such as ethyl butyrate (EB) and ethyl acetate (EA). HTL experiments, conducted at 350 °C for 60 min in a 20 mL batch reactor, include solvent-based biocrude extraction. Notably, EB showed the highest extraction yield (50.1 wt %), the lowest nitrogen distribution (5.4% with 0.32 wt %), and a remarkable 74% energy recovery (ER), setting a noteworthy benchmark in nitrogen reduction. GCMS analysis reveals EB-derived biocrude's superiority in having the least heteroatoms and nitrogenous compounds compared to hexane, EA, and DCM. Solid residues from hexane, EB, and EA displayed the highest nitrogen distribution range (62-68%), hinting at potential applications in further processes. These findings significantly inform solvent selection for efficient and sustainable waste-to-energy conversion. While promising, the study emphasizes the need to explore solvent-solute interactions further to optimize biocrude quality, highlighting the pivotal role of solvent choice in advancing clean, cost-effective waste-to-energy technologies.

Electrochemical Valorization of Glycerol via Electrocatalytic Reduction into Biofuels: A Review.

Electrochemical conversion of underutilized biomass-based glycerol into high-value-added products provides a green approach for biomass and waste valorization. Plus, this approach offers an alternative to biofuel manufacturing procedure, under mild operating conditions, compared to the traditional thermochemical routes. Nevertheless, glycerol has been widely valorized via electrooxidation, with lower-value products generated at the cathode, ignoring the electroreduction. Here, a review of the efficient glycerol reduction into various products via the electrocatalytic reduction (ECR) process was presented. This review has been built upon the background of glycerol underutilization and theoretical knowledge about the state-of-the-art ECR. The experimental understanding of the processing parameter influences towards electrochemical efficiency, catalytic activity, and product selectivity are comprehensively reviewed, based on the recent glycerol ECR studies. We conclude by outlining present issues and highlighting potential future research avenues for enhanced ECR application.

Machine Learning Model Insights into Base-Catalyzed Hydrothermal Lignin Depolymerization.

Lignin, an abundant component of plant matter, can be depolymerized into renewable aromatic chemicals and biofuels but remains underutilized. Homogeneously catalyzed depolymerization in water has gained attention due to its economic feasibility and performance but suffers from inconsistently reported yields of bio-oil and solid residues. In this study, machine learning methods were used to develop predictive models for bio-oil and solid residue yields by using a few reaction variables and were subsequently validated by doing experimental work and comparing the predictions to the results. The models achieved a coefficient of determination (R2) score of 0.83 and 0.76, respectively, for bio-oil yield and solid residue. Variable importance for each model was calculated by two different methodologies and was tied to existing studies to explain the model predictive behavior. Based on the outcome of the study, the creation of concrete guidelines for reporting in lignin depolymerization studies was recommended. Shapley additive explanation value analysis reveals that temperature and reaction time are generally the strongest predictors of experimental outcomes compared to the rest.

Prediction of Higher Heating Values in Bio-Oil from Solvothermal Biomass Conversion and Bio-Oil Upgrading Given Discontinuous Experimental Conditions.

Both the conversion of lignocellulosic biomass to bio-oil (BO) and the upgrading of BO have been the targets of many studies. Due to the large diversity and discontinuity seen in terms of reaction conditions, catalysts, solvents, and feedstock properties that have been used, a comparison across different publications is difficult. In this study, machine learning modeling is used for the prediction of final higher heating value (HHV) and ΔHHV for the conversion of lignocellulosic feedstocks to BO, and BO upgrading. The models achieved coefficient of determination (R2) scores ranging from 0.77 to 0.86, and the SHapley Additive exPlanations (SHAP) values were used to obtain model explainability, revealing that only a few experimental parameters are largely responsible for the outcome of the experiments. In particular, process temperature and reaction time were overwhelmingly responsible for the majority of the predictions, for both final HHV and ΔHHV. Elemental composition of the starting feedstock or BO dictated the upper possible HHV value obtained after the experiment, which is in line with what is known from previous methodologies for calculating HHV for fuels. Solvent used, initial moisture concentration in BO, and catalyst active phase showed low predicting power, within the context of the data set used. The results of this study highlight experimental conditions and variables that could be candidates for the creation of minimum reporting guidelines for future studies in such a way that machine learning can be fully harnessed.

Machine learning based analysis of reaction phenomena in catalytic lignin depolymerization.

Heterogeneously catalyzed lignin solvolysis opens the possibility of transforming low value biomass into high value, useful aromatic chemicals, however, its reaction behavior is poorly understood due to the many possible interactions between reaction parameters. In this study, a novel predictive model for bio-oil yield, char yield and reaction time is developed using Random Forest (RF) regression method using data available from the literature to study the impact of surface properties of the catalyst and the weight averaged molecular weight of the lignin (Mw) used in the reaction. The models achieved a coefficient of determination (R2) score of 0.9062, 0.9428 and 0.8327, respectively, and feature importance for each case was explained and tied to studies that provide a mechanistic explanation for the performance of the model. Surface properties and lignin Mw showed no importance to the prediction of bio-oil yield and average pore diameter contributed 3% of feature importance to reaction time.

Electrochemistry at chemically modified graphenes.

Electrochemical applications of graphene are of great interest to many researchers as they can potentially lead to crucial technological advancements in fabrication of electrochemical devices for energy production and storage, and highly sensitive sensors. There are many routes towards fabrication of bulk quantities of chemically modified graphenes (CMG) for applications such as electrode materials. Each of them yields different graphene materials with different functionalities and structural defects. Here, we compare the electrochemical properties of five different chemically modified graphenes: graphite oxide, graphene oxide, thermally reduced graphene oxide, chemically reduced graphene oxide, and electrochemically reduced graphene oxide. We characterized these materials using transmission electron microscopy, Raman spectroscopy, high-resolution X-ray photoelectron spectroscopy, electrochemical impedance spectroscopy, and cyclic voltammetry, which allowed us to correlate the electrochemical properties with the structural and chemical features of the CMGs. We found that thermally reduced graphene oxide offers the most favorable electrochemical performance among the different materials studied. Our findings have a profound impact for the applications of chemically modified graphenes in electrochemical devices.

Characterization of Bi and Fe co-doped PZT capacitors for FeRAM.

Ferroelectric random access memory (FeRAM) has been in mass production for over 15 years. Higher polarization ferroelectric materials are needed for future devices which can operate above about 100 °C. With this goal in mind, co-doping of thin Pb(Zr40,Ti60)O3 (PZT) films with 1 at.% Bi and 1 at.% Fe was examined in order to enhance the ferroelectric properties as well as characterize the doped material. The XRD patterns of PZT-5% BiFeO3 (BF) and PZT 140-nm thick films showed (111) orientation on (111) platinized Si wafers and a 30 °C increase in the tetragonal to cubic phase transition temperature, often called the Curie temperature, from 350 to 380 °C with co-doping, indicating that Bi and Fe are substituting into the PZT lattice. Raman spectra revealed decreased band intensity with Bi and Fe co-doping of PZT compared to PZT. Polarization hysteresis loops show similar values of remanent polarization, but square-shaped voltage pulse-measured net polarization values of PZT-BF were higher and showed higher endurance to repeated cycling up to 1010 cycles. It is proposed that Bi and Fe are both in the +3 oxidation state and substituting into the perovskite A and B sites, respectively. Substitution of Bi and Fe into the PZT lattice likely creates defect dipoles, which increase the net polarization when measured by the short voltage pulse positive-up-negative-down (PUND) method.

Surface plasmon resonance biosensor with high anti-fouling ability for the detection of cardiac marker troponin T.

Designing a surface recognition layer with high anti-fouling ability, high affinity, and high specificity is an important issue to produce high sensitivity biosensing transducers. In this study, a self-assembled monolayer (SAM) consisting of a homogeneous mixture of oligo(ethylene glycol) (OEG)-terminated alkanethiolate and mercaptohexadecanoic acid (MHDA) on Au was employed for immobilizing troponin T antibody and applied in detecting cardiac troponin T by using surface plasmon resonance (SPR). The mixed SAM showed no phase segregation and exhibited human serum albumin resistance, particularly with an antibody-immobilized surface. X-ray photoemission spectra revealed that the chemical composition ratio of OEG to the mixed SAM was 69% and the OEG packing density was 82%. The specific binding of troponin T on the designed surface indicated a good linear correlation (R=0.991, P<0.0009) at concentrations lower than 50 µgmL(-1) with the limit of detection of 100 ngmL(-1) using a SPR measuring instrument. It is concluded that the mixed SAM functions as designed since it has high detection capability, high accuracy and reproducibility, as well as shows strong potential to be applied in rapid clinical diagnosis for label-free detection within 2 min.