Abstract Silver bismuth disulfide (AgBiS 2 ) colloidal nanocrystals (CNCs) have emerged as ecofriendly photoactive materials with excellent photoconductivity and high absorption coefficients, in compliance with the restriction of hazardous substances (RoHS) guidelines. To maximize the theoretical potential of AgBiS 2 CNC solar cells, a new diketopyrrolopyrrole (DPP)‐based polymer, BD2FCT, optimized as a hole transport layer (HTL), is developed. This asymmetric thiophene‐rich polymer HTL effectively complements the optical absorption spectrum of CNCs and forms a homogeneous layer atop the CNCs, facilitating favorable vertical charge transfer through intrinsic molecular packing. Furthermore, the BD2FCT HTL aligns energetically with AgBiS 2 , significantly reducing charge recombination at the CNC/HTL interfaces and enhancing charge extraction and photocurrent generation across the entire optical absorption spectrum. These characteristics are further optimized through precise molecular engineering. Additionally, a low‐bandgap acceptor, IEICO‐4F, is structurally incorporated with the BD2FCT polymer to further improve charge funneling and complementary absorption. Transient absorption spectroscopy reveals enhanced hole transfer from CNC to BD2FCT‐29DPP:IEICO‐4F, resulting in reduced charge recombination and efficient charge extraction. Consequently, a BD2FCT‐based AgBiS 2 CNC solar cell achieves a power conversion efficiency (PCE) of 10.1%, demonstrating significant improvements in short‐circuit current density ( J SC ) and fill factor ( FF ).

When we touch an object, thermosensation allows us to perceive not only the temperature but also wetness and types of materials with different thermophysical properties (i.e., thermal conductivity and heat capacity) of objects. Emulation of such sensory abilities is important in robots, wearables, and haptic interfaces, but it is challenging because they are not directly perceptible sensations but rather learned abilities via sensory experiences. Emulating the thermosensation of human skin, we introduce an artificial thermosensation based on an intelligent thermo-/calorimeter (TCM) that can objectively differentiate types of contact materials and solvents with different thermophysical properties. We demonstrate a TCM based on pyroresistive composites with ultrahigh sensitivity (11.2% °C–1) and high accuracy (<0.1 °C) by precisely controlling the melt-induced volume expansion of a semicrystalline polymer, as well as the negative temperature coefficient of reduced graphene oxide. In addition, the ultrathin TCM with coplanar electrode design shows deformation-insensitive temperature sensing, facilitating wearable skin temperature monitoring with accuracy higher than a commercial thermometer. Moreover, the TCM with a high pyroresistivity can objectively differentiate types of contact materials and solvents with different thermophysical properties. In a proof-of-principle application, our intelligent TCM, coupled with a machine-learning algorithm, enables objective evaluation of the thermal attributes (coolness and wetness) of skincare products.

Abstract Silver bismuth disulfide (AgBiS 2 ) colloidal nanocrystals (CNCs) have emerged as ecofriendly photoactive materials with excellent photoconductivity and high absorption coefficients, in compliance with the restriction of hazardous substances (RoHS) guidelines. To maximize the theoretical potential of AgBiS 2 CNC solar cells, a new diketopyrrolopyrrole (DPP)‐based polymer, BD2FCT, optimized as a hole transport layer (HTL), is developed. This asymmetric thiophene‐rich polymer HTL effectively complements the optical absorption spectrum of CNCs and forms a homogeneous layer atop the CNCs, facilitating favorable vertical charge transfer through intrinsic molecular packing. Furthermore, the BD2FCT HTL aligns energetically with AgBiS 2 , significantly reducing charge recombination at the CNC/HTL interfaces and enhancing charge extraction and photocurrent generation across the entire optical absorption spectrum. These characteristics are further optimized through precise molecular engineering. Additionally, a low‐bandgap acceptor, IEICO‐4F, is structurally incorporated with the BD2FCT polymer to further improve charge funneling and complementary absorption. Transient absorption spectroscopy reveals enhanced hole transfer from CNC to BD2FCT‐29DPP:IEICO‐4F, resulting in reduced charge recombination and efficient charge extraction. Consequently, a BD2FCT‐based AgBiS 2 CNC solar cell achieves a power conversion efficiency (PCE) of 10.1%, demonstrating significant improvements in short‐circuit current density ( J SC ) and fill factor ( FF ).

When we touch an object, thermosensation allows us to perceive not only the temperature but also wetness and types of materials with different thermophysical properties (i.e., thermal conductivity and heat capacity) of objects. Emulation of such sensory abilities is important in robots, wearables, and haptic interfaces, but it is challenging because they are not directly perceptible sensations but rather learned abilities via sensory experiences. Emulating the thermosensation of human skin, we introduce an artificial thermosensation based on an intelligent thermo-/calorimeter (TCM) that can objectively differentiate types of contact materials and solvents with different thermophysical properties. We demonstrate a TCM based on pyroresistive composites with ultrahigh sensitivity (11.2% °C–1) and high accuracy (<0.1 °C) by precisely controlling the melt-induced volume expansion of a semicrystalline polymer, as well as the negative temperature coefficient of reduced graphene oxide. In addition, the ultrathin TCM with coplanar electrode design shows deformation-insensitive temperature sensing, facilitating wearable skin temperature monitoring with accuracy higher than a commercial thermometer. Moreover, the TCM with a high pyroresistivity can objectively differentiate types of contact materials and solvents with different thermophysical properties. In a proof-of-principle application, our intelligent TCM, coupled with a machine-learning algorithm, enables objective evaluation of the thermal attributes (coolness and wetness) of skincare products.

Polytypes of two-dimensional transition metal dichalcogenide can extend the architecture and application of nanostructures. Herein, Nb 1– x V x Se 2 alloy nanosheets in the full composition range ( x ) were synthesized by a colloidal reaction. At x = 0.1–0.3, a phase transition occurred from various hexagonal (three 2H and one 4H types) phase NbSe 2 to an atomically homogeneous 1T phase VSe 2 . Density functional theory calculations also revealed a polytypic phase transition at x = 0.3, which was shifted close to 0 in the presence of Se vacancies. Furthermore, the calculations validate favorable formation of Se vacancies at the phase transition. The sample at x = 0.3 exhibited enhanced electrocatalytic activity toward the hydrogen evolution reaction (HER) in 0.5 M H 2 SO 4 . The Gibbs free energy indicates that the catalytic HER performance is correlated with the active Se vacancy sites of polytypic structures.

We study a pharmaceutical scheduling problem with hybrid batch-continuous manufacturing process in a distributed supply chain. The supply chain consists of heterogeneous plants and one distribution center. Each plant adopts an unrelated permutation flow shop layout consisting of a hybrid batch-continuous production line. Each pharmaceutical order is split and produced in multi-production sites located in various regions. The pharmaceutical medicines manufactured by the production sites are directly shipped to a distribution center To minimize the makespan, we formulate the addressed scheduling problem as a mathematical model. To solve this model, we propose four metaheuristics variants by applying two population-based metaheuristics to two distinct solution structures. We compare the proposed metaheuristics to evaluate their performance in the numerical experiments. Additionally, we present managerial insights through sensitivity analysis.

We study a pharmaceutical scheduling problem with a hybrid batch-continuous manufacturing process in a distributed supply chain. The supply chain consists of heterogeneous plants and one distribution center. Each plant adopts an unrelated permutation flowshop layout consisting of a hybrid batch-continuous production line. Each pharmaceutical order is split and produced in multi-production sites located in various regions. The pharmaceutical medicines manufactured by the production sites are directly shipped to a distribution center. To minimize the makespan, we formulate the addressed scheduling problem as a mathematical model. To solve this model, we propose four metaheuristic variants by applying two population-based metaheuristics to two distinct solution structures. We compare the proposed metaheuristics to evaluate their performance in the numerical experiments. Additionally, we present managerial insights through sensitivity analysis.