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Polymerized ionic liquids (PILs) with anions of bis(trifluoromethylsulfonyl)imide (TFSI−), hexafluorophosphate (PF6−), and tetrafluoroborate (BF4−); and cations of poly[1-(4-vinylbenzyl)-3-alkyl-imidazolium] P[VBCnIM]+ with alkyl lengths C1, C2, C4 were successfully synthesized and characterized. X-ray scattering showed an increase of backbone-to-backbone spacing (db) by 0.8 Å per CH2 added to the alkyl side chain. Rheological and dielectric measurements were used to measure rates of chain relaxation and ion dissociation/association. The glass transition temperatures Tg follow the trend: PC4-TFSI < PC2-TFSI < PC1-TFSI< PC1-BF4 < PC1-PF6, which correlates well with their dielectric behaviors. However, the fragility mDR from dielectric relaxation increases with decreasing Tg, which is the opposite of the dependence of the fragility mRheo from rheology for both our PILs and of neutral polymers. The dielectric and rheological relaxations of our PIL’s are expected to be influenced by both their anion-cation binding energies and their relative free volumes, while for neutral polymers, only free volume influences relaxation. The increase of fragility of mDR with decreasing Tg, therefore suggests that dielectric relaxation is influenced more by anion-cation binding energy than by free volume, while the reverse is true for mRheo. The plateau modulus GN and entanglement molecular weight Me estimated from rheological measurements agree with predictions of the packing model, using only a small modification of the Flory characteristic ratio C∞ from that of a neutral polymer. Packing lengths of p= 6.0 ~ 9.3 Å and tube diameters dt= 11 ~ 17 nm are found, depending on specific cation and anion structures. 

The concept and feasibility of producing liposomes by rehydrating engineered lipid nanoconstructs are demonstrated in this study. Nanoconstructs of 1-palmitoyl-2-oleoyl-sn-glycero-3-phosphocholine (POPC) were produced using a microfluidic delivery probe integrated with an atomic force microscope. The subsequent rehydration of these POPC constructs led to the formation of liposomes, most of which remained adhered to the surface. The size (e.g., diameter) of the liposomes could be tuned by varying the lateral dimension of the lipid constructs. Hierarchical liposomal structures, such as pentagons containing five liposomes at the corners, could also be designed and produced by depositing lipid constructs to designated locations on the surfaces, followed by rehydration. This new means allows for regulating liposomal sizes, distributions, and compositions. The outcomes benefit applications of liposomes as delivery vehicles, sensors, and building blocks in biomaterials design. The ability to produce hierarchical liposomal structures benefits numerous applications such as proto-cell development, multiplexed bio-composite materials, and the engineering of local bio-environments. 

Given a set P of n points and a set S of n weighted disks in the plane, the disk coverage problem is to compute a subset of disks of smallest total weight such that the union of the disks in the subset covers all points of P. The problem is NP-hard. In this paper, we consider a line-separable unit-disk version of the problem where all disks have the same radius and their centers are separated from the points of P by a line 𝓁. We present an O(n^{3/2}log² n) time algorithm for the problem. This improves the previously best work of O(n²log n) time. Our result leads to an algorithm of O(n^{7/2}log² n) time for the halfplane coverage problem (i.e., using n weighted halfplanes to cover n points), an improvement over the previous O(n⁴log n) time solution. If all halfplanes are lower ones, our algorithm runs in O(n^{3/2}log² n) time, while the previous best algorithm takes O(n²log n) time. Using duality, the hitting set problems under the same settings can be solved with similar time complexities. 

Given a set P of n points and a set S of m disks in the plane, the disk hitting set problem asks for a smallest subset of P such that every disk of S contains at least one point in the subset. The problem is NP-hard. This paper considers a line-constrained version in which all disks have their centers on a line. We present an O(mlog²n+(n+m)log(n+m)) time algorithm for the problem. This improves the previous result of O(m²log m+(n+m)log(n+m)) time for the weighted case of the problem where every point of P has a weight and the objective is to minimize the total weight of the hitting set. Our algorithm also solves a more general line-separable problem with a single intersection property: The points of P and the disk centers are separated by a line 𝓁 and the boundary of every two disks intersect at most once on the side of 𝓁 containing P. 

Polymeric membranes have become essential for energy-efficient gas separations such as natural gas sweetening, hydrogen separation, and carbon dioxide capture. Polymeric membranes face challenges like permeability-selectivity tradeoffs, plasticization, and physical aging, limiting their broader applicability. Machine learning (ML) techniques are increasingly used to address these challenges. This review covers current ML applications in polymeric gas separation membrane design, focusing on three key components: polymer data, representation methods, and ML algorithms. Exploring diverse polymer datasets related to gas separation, encompassing experimental, computational, and synthetic data, forms the foundation of ML applications. Various polymer representation methods are discussed, ranging from traditional descriptors and fingerprints to deep learning-based embeddings. Furthermore, we examine diverse ML algorithms applied to gas separation polymers. It provides insights into fundamental concepts such as supervised and unsupervised learning, emphasizing their applications in the context of polymer membranes. The review also extends to advanced ML techniques, including data-centric and model-centric methods, aimed at addressing challenges unique to polymer membranes, focusing on accurate screening and inverse design. 

Abstract Abnormal cancer metabolism causes hypoxic and immunosuppressive tumor microenvironment (TME), which limits the antitumor efficacy of photodynamic therapy (PDT). Herein, we report a photosensitizing nanoscale metal–organic layer (MOL) with anchored 3‐bromopyruvate (BrP), BrP@MOL, as a metabolic reprogramming agent to enhance PDT and antitumor immunity. BrP@MOL inhibited mitochondrial respiration and glycolysis to oxygenate tumors and reduce lactate production. This metabolic reprogramming enhanced reactive oxygen species generation during PDT and reshaped the immunosuppressive TME to enhance antitumor immunity. BrP@MOL‐mediated PDT inhibited tumor growth by >90 % with 40 % of mice being tumor‐free, rejected tumor re‐challenge, and prevented lung metastasis. Further combination with immune checkpoint blockade potently regressed the tumors with >98 % tumor inhibition and 80 % of mice being tumor‐free.