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The performance of phosphorus-based lubricant additives is governed by their adsorption, stability, and reactivity at the metal interface. In this study, we investigate the adsorption behavior and tribochemical stability of three additives: Octyl Acid Phosphate (OAP), Dibutyl Hydrogen Phosphite (DBHP), and Amine Neutralized Acid Phosphate (ANAP). These additives are studied on iron and hematite surfaces using both ab initio calculations and experimental analyses on steel. Simulations revealed that ANAP exhibited the strongest adsorption on iron, followed by DBHP, while OAP showed weaker interactions, though its chemisorption was enhanced on hematite via hydrogen loss. Under tribological conditions, the DBHP phosphite dissociated more readily than the other two phosphates molecules due to its lower phosphorus coordination, as confirmed by bond order analysis. Quartz crystal microbalance (QCM) measurements indicated significant differences in adsorption behavior across temperatures, with DBHP forming stable deposits, while ANAP exhibited poor retention, in agreement with ab initio molecular dynamics simulations. X-ray photoelectron spectroscopy (XPS) confirmed DBHP's strong chemisorp

One technique for creating semiconductor crystals with new, desired properties involves replacing some atoms in the crystal lattice with additives - atoms of a different type. This substitution not only alters the bulk properties of the crystal but also affects the patterns formed on its surface. A surface that is smooth and regular in a uniform crystal can become bunched or meandered under the same growth conditions if some atoms are replaced by additives. The Vicinal Cellular Automaton (VicCA) model is used to study this behavior, analyzing the mechanism of pattern formation when additives are introduced into the system. It has also been shown that the newly formed structures resulting from the presence of additives can be smoothed by applying successive layers of a homogeneous composition on top for a sufficiently long time. Additives can also act as smoothing agents for bunched or meandered surface patterns that develop in a homogeneous crystal. However, their effectiveness diminishes for spatially extended patterns such as nanowires. Typically, when homogeneous crystal layers are applied to spatial structures, the resulting surface is not entirely smoothed but instead transfor

Exploring the impact of alcohol additives on combustion and pyrolysis of ammonia/methane is of great importance in the pursuit of sustainable energy technologies. This work employs Reactive Force Field (ReaxFF) molecular dynamics (MD) simulations to investigate the underlying mechanism of how ethanol and methanol additives affect reaction pathways, NOx emissions and bond energy characteristics in ammonia-methane pyrolysis and combustion processes. It shows that adding alcohols altered NOx formation pathways, reducing the diversity of NOx and shifting the equilibrium toward simpler NOx such as NO and NO2. At 2,000 K, alcohol blends, particularly methanol, demonstrated a notable reduction in NO2 formation. At 3,000 K, both ethanol and methanol suppressed NO production, but the influence of methanol was stronger. Nitric acid production, HNO3, was present at lower temperatures but became negligible at higher temperatures because of the thermal breakdown of the higher-order NOx. These trends confirm that alcohol additives realize a probable role in moderating NOx emissions and stabilizing reaction pathways. The pyrolysis in modified reaction pathways, which facilitated the decomposition

We investigated the superconducting (SC) properties of Ta-Nb-Hf-Zr-Ti high-entropy alloy (HEA) thin films with 0.5% weight C additives. The C additives stabilize the structural properties and enhance the SC critical properties, including $μ_0$Hc$_2$ (13.45 T) and Tc (7.5 K). The reflectance of the C-added HEA film is enhanced in the low-energy region, resulting in a higher optical conductivity, which is consistent with the lower electrical resistivity. In addition, we observed SC vortices in the C-added HEA film using magnetic force microscopy. The magnetic penetration depths ($λ$) of the pure HEA and C-added HEA films were estimated from their Meissner force curves by comparing them with those of a reference Nb film. At 4.2 K, the λ of the C-added film is 360 nm, shorter than that of the pure HEA film (560 nm), indicating stronger superconductivity against an applied magnetic field.

Phosphorus-based lubricant additives are used for protecting metallic contacts under boundary lubrication by forming surface films that reduce wear and friction. Despite their importance, the molecular mechanisms driving their friction-reducing effects remain unclear, especially for phosphate esters, whose molecular structure critically impact tribological behavior. In this study, we use machine learning-based molecular dynamics simulations to investigate the tribological performance of three representative phosphorus-based additives, Dibutyl Hydrogen Phosphite (DBHP), Octyl Acid Phosphate (OAP), and Methyl Polyethylene Glycol Phosphate (mPEG-P), on iron surfaces. The mPEG-P family is further analyzed by varying esterification degree and chain length. DBHP exhibits the lowest friction and largest interfacial separation, resulting from steric hindrance and tribochemical reactivity, as indicated by P-O bond cleavage and enhanced O-Fe interactions. In contrast, OAP and mPEG-P monoesters produce higher friction due to limited steric protection and reduced resistance to shear, leading to partial loss of surface coverage under extreme conditions. Within the mPEG-P family, multi-ester and

Perovskite solar cells (PSCs) have rapidly emerged as a leading contender in next-generation photovoltaic technologies, owing to their exceptional power conversion efficiencies and advantageous material properties. Despite these advances, challenges such as long-term stability, environmental sustainability, and scalable manufacturing continue to hinder their commercialization. Precursor additive engineering has shown promise in addressing these issues by enhancing both the performance and durability of PSCs. However, the explosive growth of scientific literature and the complex interplay of materials, processes, and device architectures make it increasingly difficult for researchers to efficiently access, organize, and utilize domain knowledge in this rapidly evolving field. To address this gap, we introduce Perovskite-R1, a specialized large language model (LLM) with advanced reasoning capabilities tailored for the discovery and design of PSC precursor additives. By systematically mining and curating 1,232 high-quality scientific publications and integrating a comprehensive library of 33,269 candidate materials, we constructed a domain-specific instruction-tuning dataset using aut

This paper investigates the thermoelectric properties of solid polymer electrolytes (SPE) containing lithium bis(trifluoromethanesulfonyl)imide (LiTFSI) and sodium bis(trifluoromethanesulfonyl)imide (NaTFSI) salts, along with carbon-based additives of various dimensionalities. Increasing salt concentration leads to higher Seebeck coefficients as a result of the increasing number of free charge carriers and additional, superimposed effects by ion-ion and ion-polymer interactions. NaTFSI-based electrolytes exhibit negative Seebeck coefficients (up to $S = -1.5\,\mathrm{mV\,K^{-1}}$), indicating dominant mobility of $\mathrm{TFSI^-}$ ions. Quasi-one-dimensional carbon nanotubes (CNTs) increase the Seebeck coefficient by a factor of 3. Planar, two-dimensional graphite flakes (GF) moderately enhance it, affecting $\mathrm{Na^+}$ and $\mathrm{TFSI^-}$ ion mobilities and electronic conductivity. Bulky, three-dimensional carbon black (CB) additives induce a unique behavior where the sign of the Seebeck coefficient changes with temperature, presumably due to interaction with $\mathrm{TFSI^-}$ ions within the CB structure. Changes in activation energy and Vogel temperature with salt concentr

Incorporation of guest additives within inorganic single crystals offers a unique strategy for creating nanocomposites with tailored properties. While anionic additives have been widely used to control the properties of crystals, their effective incorporation remains a key challenge. Here, we show that cationic additives are an excellent alterative for the synthesis of nanocomposites, where they are shown to deliver exceptional levels of incorporation of up to 70 wt% of positively charged amino acids, polymer particles, gold nanoparticles, and silver nanoclusters within inorganic single crystals. This high additive loading endows the nanocomposites with new functional properties including plasmon coupling, bright fluorescence, and surface-enhanced Raman scattering (SERS). Cationic additives are also shown to outperform their acidic counterparts, where they are highly active in a wider range of crystal systems, owing to their outstanding colloidal stability in the crystallization media and strong affinity for the crystal surfaces. This work demonstrates that although often overlooked, cationic additives can make valuable crystallization additives to create composite materials with t

We address the problem of classifying bird species using their song recordings, a challenging task due to environmental noise, overlapping vocalizations, and missing labels. Existing models struggle with low-SNR or multi-species recordings. We hypothesize that birds can be classified by visualizing their pitch pattern, speed, and repetition, collectively called motifs. Deep learning models applied to spectrogram images help, but similar motifs across species cause confusion. To mitigate this, we embed frequency information into spectrograms using primary color additives. This enhances species distinction and improves classification accuracy. Our experiments show that the proposed approach achieves statistically significant gains over models without colorization and surpasses the BirdCLEF 2024 winner, improving F1 by 7.3%, ROC-AUC by 6.2%, and CMAP by 6.6%. These results demonstrate the effectiveness of incorporating frequency information via colorization.

Molybdenum dithiocarbamates (MoDTCs) are lubricant additives very efficient in reducing the friction of steel and they are employed in a number of industrial applications. The functionality of these additives is ruled by the chemical interactions occurring at the buried sliding interface, which are of key importance for the improvement of the lubrication performance. Yet, these tribochemical processes are very difficult to monitor in real time. Ab initio molecular dynamics simulations are the ideal tool to shed light into such a complicated reactivity. In this work we perform ab initio simulations, both in static and tribological conditions, to understand the effect of surface oxidation on the tribochemical reactivity of MoDTC and we find that when the surfaces are covered by oxygen, the first dissociative steps of the additives are significantly hindered. Our preliminary tribological tests on oxidized steel discs support these results. Bare metallic surfaces are necessary for a stable adsorption of the additives, their quick decomposition, and the formation of a durable MoS$_2$ tribolayer. This work demonstrates the importance of the catalytic role of the substrate and confirms th

Machine learning (ML) has been extensively employed in planar perovskite photovoltaics to screen effective organic molecular additives, while encountering predictive biases for novel materials due to small datasets and reliance on predefined descriptors. Present work thus proposes an effective approach, Co-Pilot for Perovskite Additive Screener (Co-PAS), an ML-driven framework designed to accelerate additive screening for perovskite solar cells (PSCs). Co-PAS overcomes predictive biases by integrating the Molecular Scaffold Classifier (MSC) for scaffold-based pre-screening and utilizing Junction Tree Variational Autoencoder (JTVAE) latent vectors to enhance molecular structure representation, thereby enhancing the accuracy of power conversion efficiency (PCE) predictions. Leveraging Co-PAS, we integrate domain knowledge to screen an extensive dataset of 250,000 molecules from PubChem, prioritizing candidates based on predicted PCE values and key molecular properties such as donor number, dipole moment, and hydrogen bond acceptor count. This workflow leads to the identification of several promising passivating molecules, including the novel Boc-L-threonine N-hydroxysuccinimide ester

The presence of a tiny amount of polymers (a few parts per million) in a fluid dramatically reduces turbulent drag. For this reason, polymer additives have been proposed to be used in flood remediation: in an open channel at a fixed flow rate, the decrease in friction due to polymer addition is expected to lead to a decrease in water height in the channel. However, in a recent field experiment, a counterintuitive transient increase in water height has been observed far downstream of polymer injection. We numerically investigate the effect of polymer additives on the water height in a long canal using the shallow water equations augmented with an evolution equation for polymer concentration that incorporates turbulent dispersion and polymer degradation. Just downstream of polymer injection, the water height decreases due to the decreased friction at a fixed volumetric flow rate. Further downstream, however, the height can increase, consistent with the experimental observation. We elucidate the mechanism of this unexpected rise in water height in the canal. We also suggest a technique to mitigate the water rise, as this is detrimental for practical applications.

This work concerns the proof theory of (left) skew monoidal categories and their variants (e.g. closed monoidal, symmetric monoidal), continuing the line of work initiated in recent years by Uustalu et al. Skew monoidal categories are a weak version of Mac Lane's monoidal categories, where the structural laws are not required to be invertible, they are merely natural transformations with a specific orientation. Sequent calculi which can be modelled in such categories can be identified as deductive systems for restricted substructural fragments of intuitionistic linear logic. These calculi enjoy cut elimination and admit a focusing strategy, sharing resemblance with Andreoli's normalization technique for linear logic. The focusing procedure is useful for solving the coherence problem of the considered categories with skew structure. Here we investigate possible extensions of the sequent calculi of Uustalu et al. with additive connectives. As a first step, we extend the sequent calculus with additive conjunction and disjunction, corresponding to studying the proof theory of skew monoidal categories with binary products and coproducts satisfying a left-distributivity condition. We int

In this study methods of statistical thermodynamics were applied to white tin - grey tin equilibrium to find if there is any correlation between Debye temperature of an additive and its effect on the equilibrium. Gibbs free energy of the transition was calculated for a composite system comprised of tin and the additive in assumption of Debye temperature of the composite system being a linear combination of Debye temperatures of the components with their respective molar fractions as weighing coefficients. Calculations showed that additives which Debye temperatures are above 214oK should stabilize grey tin, while the additives with Debye temperatures below 214oK should stabilize the white one. Devised rule corroborates with all previously reported experimental data on the effect of different additives on the transition. Predictions of the rule about non reported additives provide a mean for its experimental verification.

In the present work we calculate the non-linear additives to the Brooks-Herring electrostatic screened potential. We also calculate the corresponding additives to the ionized impurity scattering mobility in n-GaAs over a wide range of doping levels and temperatures. It is demonstrated that the additives to the mobility changes its value up to 50%. The most dramatically mobility is changed near the Mott transition, when the doping level is about $10^{14}$ - $2\times10^{16}$ cm$^{-3}$ and temperature is below 50 K. Also, the results allow us to conclude that the non-linear additives break the cross section symmetry with respect to the sign of the charge of the scattering center.

We study rain water infiltration and drainage in a dry model sandy soil with superabsorbent hydrogel particle additives by measuring the mass of retained water for non-ponding rainfall using a self-built 3D laboratory set-up. In the pure model sandy soil, the retained water curve measurements indicate that instead of a stable horizontal wetting front that grows downward uniformly, a narrow fingered flow forms under the top layer of water-saturated soil. This rain water channelization phenomenon not only further reduces the available rain water in the plant root zone, but also affects the efficiency of soil additives, such as superabsorbent hydrogel particles. Our studies show that the shape of the retained water curve for a soil packing with hydrogel particle additives strongly depends on the location and the concentration of the hydrogel particles in the model sandy soil. By carefully choosing the particle size and distribution methods, we may use the swollen hydrogel particles to modify the soil pore structure, to clog or extend the water channels in sandy soils, or to build water reservoirs in the plant root zone.

We present experimental evidence that a minute amount of polymer additives can significantly enhance heat transport in the bulk region of turbulent thermal convection. The effects of polymer additives are found to be the \textit{suppression} of turbulent background fluctuations that give rise to incoherent heat fluxes that make no net contribution to heat transport, and at the same time to \textit{increase} the coherency of temperature and velocity fields. The suppression of small-scale turbulent fluctuations leads to more coherent thermal plumes that result in the heat transport enhancement. The fact that polymer additives can increase the coherency of thermal plumes is supported by the measurements of a number of local quantities, such as the extracted plume amplitude and width, the velocity autocorrelation functions and the velocity-temperature cross-correlation coefficient. The results from local measurements also suggest the existence of a threshold value for the polymer concentration, only above which can significant modification of the plume coherent properties and enhancement of the local heat flux be observed. Estimation of the plume emission rate suggests that the second

Combustion process can become more energy efficient and environment friendly if used with appropriate fuel additive. Discovery of fuel additive can be accelerated by applying hybrid approach of using of chemical kinetics and Machine Learning (ML). In this work, we present a framework that takes the robustness of Machine Learning and accuracy of chemical kinetics to predict the effect of fuel additive on autoignition process. We present a case of making predictions for Ignition Delay Time (IDT) of biofuel n-butanol ($C_4H_9OH$) with several fuel additives. The proposed framework was able to predict IDT of autoignition with high accuracy when used with unseen additives. This framework highlights the potential of ML to exploit chemical mechanisms in exploring and developing the fuel additives to obtain the desirable autoignition characteristics.