Binding energies of adsorbed CO and H are key descriptors governing the activity and selectivity of the co-electrolysis of CO2 and H2O to produce syngas with desired CO/H2 ratios. Palladium hydride (PdH), which forms in situ at negative overpotentials, has been identified as the active Pd phase for CO2 reduction to syngas. Herein, binding energies of CO and H are determined using temperature programmed desorption (TPD) of CO and H2 from Pd(111), PdH/Pd(111), and Cu/PdH/Pd(111) under ultra-high vacuum (UHV) conditions. TPD results reveal that desorption of H2 from subsurface PdH occurs at 460 K, while desorption from surface PdH is more facile at 320 K. CO desorption temperatures shift 20 K lower on PdH/Pd(111) compared to on Pd(111). The presence of 0.7 ML Cu further increases the desorption temperature of H2 by 30 K while simultaneously reducing CO desorption temperatures by 70 K. Density functional theory (DFT) calculations show that CO adsorption onto Pd sites is hindered on the 0.7 ML Cu/PdH/Pd(111) surface while the kinetic barrier for H2 desorption is increased. The trends in the binding energies of CO and H on model surfaces are consistent with electrochemical measurements of CuPd powder catalysts in a membrane electrode assembly (MEA), where H2 evolution is reduced while CO production is enhanced compared to unmodified Pd catalysts. Overall, the results from model surface studies (TPD and DFT) provide a prediction and explanation for the activity and CO/H2 ratios observed in electrochemical experiments. This study also demonstrates that CuPd is a promising catalyst with reduced Pd-loading to produce CO-rich syngas.

Nanoconfined electrolytes exhibit distinct structural and transport properties critical for emerging energy storage and nanofluidic technologies. Graphite nanochannels, with their atomically smooth surfaces and ionophilicity, serve as ideal model systems to explore ion-specific interactions under extreme confinement. To investigate the ionophilicity of graphite, we performed molecular dynamics simulations on four aqueous electrolytes-LiCl, LiTFSI, NaCl, and NaTFSI-confined within 0.89 nm graphite slit pores. For LiCl and LiTFSI, additional simulations across a wide range of concentrations up to the water-in-salt regime were conducted. In agreement with experiments, at low concentrations, the channels show a clear anion selectivity: TFSI--based electrolytes exhibit strong ion enrichment within the channel, whereas Cl--based systems display significantly weaker adsorption. This preference is, however, notably reduced at higher concentrations, where ionic distributions become more uniform regardless of anion type. These results are rationalized in terms of capillary pressure and liquid viscosity, which ultimately govern the channel ions uptake. Our findings provide molecular-level insights into how ion-specific interactions regulate transport dynamics in narrow graphite channels, offering guidance for designing efficient nanoconfined electrochemical systems.

When the size of the confinement approaches that of individual molecules and ions at the angstrom-scale (Å-scale), intriguing ion-ion and water-ion interactions emerge. Water, being a unique solvent, has asymmetric dielectric constants and discrete configurations within Å-scale confinement. However, how these distinctive characteristics of water affect ion behaviors as well as how they translate to other solvents remains largely unexplored. Here, with the robust platform of functionalized two-dimensional molybdenum disulfide nanochannels, we systematically probed how the solvent environment regulates ion dynamics, uptake, and selectivity in Å-scale confinement, using rare-earth elements (REEs) as a model system due to their periodic properties and practical relevance. By tuning solvent composition, we find that both ion uptake and selectivity are governed by the interplay between binding-site deprotonation controlled by solvent-dependent acidity and dielectric effects mediated by the solvent environment. Maximum uptake was observed at intermediate solvent ratios where acidity and dielectric properties are balanced. Increasing deprotonation in dimethylformamide-rich systems shifts selectivity toward heavier REEs. Dynamic tests further reveal that the preference for heavy element Yb arises from stronger binding and improved channel accessibility, facilitated by carboxylate formation and lighter-element-assisted channel opening. Collectively, these findings highlight solvent composition as a powerful lever for tuning ion behavior in 2D channels, and provide mechanistic insight into solvent-mediated approaches to selective REE recovery and separation.

Future materials should be made from renewable resources and be sustainable without compromising the mechanical properties compared to conventional products. Kraft lignin is an available renewable raw material, sourced globally as a by-product from paper pulp production, and currently burnt at a low value. Kraft lignin has been converted into thermoplastics, however the mechanical properties worsen by degree of blending. Thermosets containing kraft lignin give materials with high strength, where the lignin matrix contributes to the mechanical properties. However, pre-fractionation or multistep chemistries have been applied to give high performance materials. Herein, we have combined kraft lignin with bio-based glycerol 1,3-diglycidyl ether to give a resin with enhanced mechanical properties. This resin - LigniSet® - is odorless, which is a unique property for kraft lignin-based products. The resin is, due to its hydrophilicity, compatible with natural fibers to give strong composite materials. The material can be recycled to give new materials without reduction in performance. Life cycle assessment shows that transformation of lignin to materials instead of burning shows significant benefits with respect to environmental sustainability.

This work offers a side-by-side overview of the behaviour of two liquors obtained via two fractionation processes, ionosolv and organosolv, from Eucalyptus globulus wood, and how the precipitation strategy that follows may affect the final yield, morphology, and particle size of every kind of lignin nanoparticle. For lignin nanoparticle precipitation, two bottom-up techniques and two top-down approaches were employed to determine which combination of fractionation process and synthesis treatment would provide the nanoparticles with the best characteristics. The results demonstrated the importance of the fractionation process in the final lignin nanoparticle yield, as ionosolv fractionation gave enhanced yields of more than 60% lignin in the form of nanoparticles. However, sphericity, particle sizes, and non-agglomerated structures were easily obtained from organosolv liquors, in which precipitation was carried out progressively in the absence of sonication. The use of ultrasound mostly resulted in the breakage of particles into smaller and irregular pieces. However, in the case of ionosolv liquors, homogeneous spherical nanoparticles were fused, forming agglomerates of smaller particles through the top-down strategy of complete addition of the antisolvent followed by sonication. The highest precipitation yield of nanoparticles was obtained from ionosolv liquors in which the full amount of antisolvent was added in one step to precipitate lignin, and then sonication was applied. In contrast, the lignin nanoparticles (LNPs) precipitation strategy that resulted in more spherical LNPs was the bottom-up strategy of precipitation by progressive antisolvent addition, resulting in visually observed non-aggregated spherical particles with a particle size distribution of 200 nm < dp < 500 nm, molecular weight of Mw = 14 000 g mol-1, and thermal degradation property of T10% = 310 °C.

Fabrication of remote-controlled ion and molecule delivery devices with sub-nanomolar accuracy possesses enormous potential for application in futuristic areas like bio-sensing, molecular machines, energy storage/harvesting, separation, and purification. Here, we combined the excellent photothermal characteristics of oxidised multiwalled carbon nanotubes (o-CNT) with atomically thin 2D sheets of V2O5 (VO) to fabricate fluidic devices capable of discharging ions and molecules with sub-nanomolar accuracy. Remarkably, the delivery of ionic/molecular pulses can be remotely modulated through irradiation with IR light. The heterostructured fluidic membrane (HFM), fabricated as a bilayer membrane with distinctive regions of o-CNT and VO, can continuously deliver K+ ions at the rate of ∼0.58 nmol min-1. The ionic flow rate can be further tuned by modulating the thickness and composition of the HFM. Moreover, triangular HFM can discharge ions in nanomolar pluses (∼1.85 to 6.92 nmol per pulse) when remotely triggered through light of different intensities. Likewise, the intermixed fluidic membrane (IFM), fabricated through vacuum filtration of the homogeneous mixtures of the VO and o-CNT, can discharge biologically relevant molecules like tryptophan (Trp) and aspirin at a rate as low as 0.1 nmol min-1, which can be further modulated with light and heat.

Sum frequency generation (SFG) spectroscopy was applied to investigate D2O adsorption on atomic layer deposition (ALD)-grown Al2O3, ZrO2, and TiO2 films at 94 ± 1 K. Film composition and thickness were characterized by ellipsometry and X-ray photoelectron spectroscopy (XPS). Additional SFG measurements were conducted on the SiO2/Si wafer and on a CoO film prepared by oxidizing Co foil. At D2O exposure below 3000 L, the spectra were dominated by interfacial features originating from the ice-oxide interface. These spectra exhibited a weak, broad O-D stretching band (OD3) centered at 2650 cm-1, tentatively attributed to (dissociated) water molecules hydrogen-bonded to the oxide surface; this assignment was supported by the absence of the OD3 feature on the SiO2/Si substrate. A sharp peak at 2730 cm-1 was also observed and assigned to the "free" O-D stretch (non-hydrogen-bonded with any neighboring molecule) of surface D2O molecules pointing into the vapor phase. Upon increasing D2O exposure, both the OD3 and "free" OD bands decreased in intensity and were replaced by weakly hydrogen-bonded OD2 and strongly hydrogen-bonded OD1 modes associated with the ice-vapor interface. As the exposure increased further, the OD2 and OD1 bands shifted to lower wavenumbers (2310 to 2284 cm-1) and became stronger, with the OD1 mode exhibiting a larger red shift and more pronounced intensity enhancement. No significant differences in water structure were observed on the Al2O3, ZrO2, and CoO films at the ice-vapor interfaces, apart from an approximately fivefold reduction in intensity on CoO, which is attributed to signal scattering from the rough CoO film/Co foil surface. However, when D2O exposure reached ≥30 000 L, the OD1 band on the TiO2 surfaces decreased substantially in intensity and shifted to much lower wavenumbers (2065 cm-1 at 30 000 L; 2030 cm-1 at 102 000 L) than on Al2O3 (2283 cm-1 at 90 000 L), ZrO2 (2293 cm-1 at 30 000 L), and CoO (2284 cm-1 at 900 000 L), indicating specific hydrogen-bonding interactions on the TiO2 surface.

Alfred Ludwig opened the discussion of the paper by Varun Chaudhary: I am missing a comparison between your material and other established magnetic materials; your observed coercivity and saturation/magnetisation values should be compared with benchmarks. With the very low Curie temperatures you find, could these

Multicomponent phase space is enormous and contains a vast number of complex new materials. Despite intensive investigation in the last decade and a half however, we are only slowly making progress towards understanding these new materials. This paper attempts to summarise some of the fundamental discoveries we have made about the geography of multicomponent phase space and the wide range of complex new materials that we have found within it. This paper discusses briefly the following topics: the size and shape of multicomponent phase space and the range of single- and multiple-phase fields that it contains; the (initially) surprising presence of many large near-ideal single-phase solid-solution phases, stabilised by a high configurational entropy of mixing; the extensive and wide-ranging variation of local nanostructure and associated mechanical and electronic lattice strain that permeates throughout high-entropy solid-solution phases; and some of the unusual, exciting and valuable properties that are then produced within multicomponent and high-entropy materials. Many of the results discussed have been obtained from the fcc Cantor alloys (based on the original Cantor alloy, equiatomic fcc CrMnFeCoNi) and the bcc Senkov alloys (based on the original Senkov alloy, equiatomic VNbMoTaW), two groups of multicomponent high-entropy single-phase materials that have been particularly widely studied. Similar behaviour is also found in other multicomponent high-entropy single-phase materials, though these have not been studied so intensively. In comparison with multicomponent high-entropy single-phase materials, rather little is known about multicomponent multiphase materials that have also not been studied so intensively.

Creating and curating new data to augment heuristics is a forthcoming approach to materials science in the future. Highly improved properties are advantageous even with "commodity polymers" that do not need to undergo new synthesis, high-temperature processes, or extensive reformulation. With artificial intelligence and machine learning (AI/ML), optimizing synthesis and manufacturing methods will enable higher throughput and innovative directed experiments. Simulation and modeling to create digital twins with statistical and logic-derived design, such as the design of experiments (DOE), will be superior to trial-and-error approaches when working with polymer materials. This paper describes and demonstrates protocols for understanding hierarchical approaches in optimizing the polymerization and copolymerization process via AI/ML to target specific properties, using model monomers such as styrene and acrylate. The key is self-driving continuous flow chemistry reactors with sensors (instruments) and real-time ML with an online monitoring set-up that allows a feedback loop mechanism. We provide initial results using ML refinement of the classical Mayo-Lewis equation (MLE), time-series data, and an autonomous flow reactor system build-up as a future data-generating station. More importantly, it lays the ground for precision control of the copolymerization process. In the future, it should be possible to undertake collaborative human-AI-guided protocols for the autonomous fabrication of new polymers guided by literature and available data sources targeting new properties.