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Interface engineering is the key to optimizing optoelectronic device performance, addressing challenges like reducing potential barriers, passivating interface traps, and controlling recombination of charges. Metal fluorides such as lithium fluoride are employed in interface modification within organic devices due to their strong dipole characteristics but carry health risks, high processing costs, and minimal impact on interface traps in organic electronics. Hence, this study investigates alternative metal chloride (MC) nanocrystals (sodium, cesium, rubidium, and potassium chlorides) that exhibit a strong dipole moment and are readily processable with the aim of reducing the influence of interface traps. Interfacial properties are assessed via various techniques, including electron paramagnetic resonance, X-ray/ultraviolet photoelectron spectroscopy, capacitance-voltage measurements, and density functional theory calculations. In organic light-emitting diodes (OLEDs), the influence of MC on charge transfer, trap density, and light emission properties is evaluated. MCs in ZnO:PEIE nanocomposites (NCs) show improved charge transport, accelerated trapping/detrapping in ZnO:PEIE NCs, and a 50% reduction in active traps in NaCl-based devices versus the reference without MCs. RbCl-, CsCl-, and NaCl-based OLEDs exhibit substantial reductions in the potential barrier between the electron injection layer and the metal contact (Al) from 4.43 to 2.93, 3.02, and 4 eV, respectively, accompanied by enhancements of 35, 27, and 25% in electroluminescence intensity.

Sodium-ion batteries (SIBs) are a more sustainable alternative to lithium-ion batteries (LIBs) considering the abundance, global distribution, and low cost of sodium. However, their economic impact remains small compared to LIBs, owing in part to the lag in materials development where significant improvements in energy density and safety remain to be realized. Deep eutectic solvents (DESs) show promise as alternatives to conventional electrolytes in SIBs because of their nonflammable nature. However, their practical application has thus far been hindered by their limited electrochemical stability window. In particular, DESs based on N -methylacetamide have thus far been reported not to be stable with sodium metal. In contrast, this work reports a superconcentration strategy where sodium-ion conducting DESs, based on the dissolution of NaFSI in N -methylacetamide, are simultaneously stable with sodium metal and Prussian blue as state-of-the-art positive electrode material. At 60 °C, the nonflammable DES outperforms a conventional liquid electrolyte in terms of rate performance and capacity retention. Therefore, these novel DES compositions pave the way for the use of DESs in practical applications with an improved safety and sustainability., Competing Interests: The authors declare no competing financial interest., (© 2024 The Authors. Published by American Chemical Society.)

Driven by expanding interest in battery storage solutions and the success story of lithium-ion batteries, the research for the discovery and optimization of new battery materials and concepts is at peak. The generation of experimental (dis)charge data using coin cells is fast and feasible and proves to be a favorite practice in the battery research labs. The quantitative interpretation of the data, however, is not trivial and decelerates the process of screening and optimization of electrode materials and recipes. Here, we introduce the concept of polarographic map and demonstrate how it can be leveraged to quantify the contribution of different non-equilibrium phenomena to the performance limitation and total polarization of a lithium-ion cell. We showcase the accuracy and diagnostic power of this approach by preparing and analyzing the electrochemical performance of 54 sets of LiNi x Mn y Co 1-x-y O 2 electrodes with different formulations and designs discharged in a range of 0.2C-5C., Competing Interests: The authors declare that there is no conflict of interest., (© 2021 The Author(s).)

In situ nitrogen doping of aluminum phosphate has been investigated in two different plasma-enhanced atomic layer deposition (PE-ALD) processes. The first method consisted of the combination of trimethyl phosphate plasma (TMP*) with a nitrogen plasma and trimethyl aluminum (TMA), that is, TMP*-N 2 *-TMA. The second method replaces TMP* with a diethylphosphoramidate plasma (i.e., DEPA*-N 2 *-TMA) of which the amine group could further aid nitrogen doping and/or eliminate the need for a nitrogen plasma step. At a substrate temperature of 320 °C, the TMP*-based process showed saturated growth (0.8 nm/cycle) of a nitrogen-doped (approximately 8 atom %) Al phosphate, while the process using DEPA* showed a similar amount of nitrogen but a significantly higher growth rate (1.4 nm/cycle). In the latter case, nitrogen doping could also be achieved without the nitrogen plasma, but this leads to a high level of carbon contamination. Both films were amorphous as-deposited, while X-ray diffraction peaks related to AlPO 4 appeared after annealing in a He atmosphere. For high coating thickness (>2 nm), a significant increase in the Li-ion transmittance was found after nitrogen doping, although the coating has to be electrochemically activated. At lower thickness scales, such activation was not needed and nitrogen doping was found to double the effective transversal electronic conductivity. For the effective transversal ionic conductivity, no conclusive difference was found. When a lithium nickel manganese cobalt oxide (NMC) powder is coated with one ALD cycle of N-doped Al phosphate, the rate capability and the energy efficiency of the electrode improves.

Liposomes are known to be promising nanoparticles (NPs) for drug delivery applications. Among different types of self-assembled NPs, liposomes stand out for their non-toxic nature, and their possession of dual hydrophilic-hydrophobic domains. Advantages of liposomes include the ability to solubilize hydrophobic drugs, the ability to incorporate different hydrophilic and lipophilic drugs at the same time, lessening the exposure of host organs to potentially toxic drugs and allowing modification of the surface by a variety of different chemical groups. This modification of the surface, or of the individual constituents, may be used to achieve two important goals. Firstly, ligands for active targeting can be attached that are recognized by cognate receptors over-expressed on the target cells of tissues. Secondly, modification can be used to impart a stimulus-responsive or "smart" character to the liposomes, whereby the cargo is released on demand only when certain internal stimuli (pH, reducing agents, specific enzymes) or external stimuli (light, magnetic field or ultrasound) are present. Here, we review the field of smart liposomes for drug delivery applications.

A new more efficient variant of a recently developed algorithm for unsupervised fuzzy clustering is introduced. A Weighted Incremental Neural Network (WINN) is introduced and used for this purpose. The new approach is called FC-WINN (Fuzzy Clustering using WINN). The WINN algorithm produces a net of nodes connected by edges, which reflects and preserves the topology of the input data set. Additional weights, which are proportional to the local densities in input space, are associated with the resulting nodes and edges to store useful information about the topological relations in the given input data set. A fuzziness factor, proportional to the connectedness of the net, is introduced in the system. A watershed-like procedure is used to cluster the resulting net. The number of the resulting clusters is determined by this procedure. Only two parameters must be chosen by the user for the FC-WINN algorithm to determine the resolution and the connectedness of the net. Other parameters that must be specified are those which are necessary for the used incremental neural network, which is a modified version of the Growing Neural Gas algorithm (GNG). The FC-WINN algorithm is computationally efficient when compared to other approaches for clustering large high-dimensional data sets.