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Flexible butyl interconnection segments are synthetically incorporated into an electronically conductive poly(pyrene methacrylate) homopolymer and its copolymer. The insertion of butyl segment makes the pyrene polymer more flexible, and can better accommodate deformation. This new class of flexible and conductive polymers can be used as a polymer binder and adhesive to facilitate the electrochemical performance of a silicon/graphene composite anode material for lithium ion battery application. They act like a “spring” to maintain the electrode mechanical and electrical integrity. High mass loading and high areal capacity, which are critical design requirements of high energy batteries, have been achieved in the electrodes composed of the novel binders and silicon/graphene composite material. A remarkable area capacity of over 5 mAh/cm2 and volumetric capacity of over 1700 Ah/L have been reached at a high current rate of 333 mA/g.

A search is presented for new physics in events with two low-momentum, oppositely charged leptons (electrons or muons) and missing transverse momentum in proton-proton collisions at a centre-of-mass energy of 13 TeV. The data collected using the CMS detector at the LHC correspond to an integrated luminosity of 35.9. The observed event yields are consistent with the expectations from the standard model. The results are interpreted in terms of pair production of charginos and neutralinos (X1 and X2) with nearly degenerate masses, as expected in natural supersymmetry models with light higgsinos, as well as in terms of the pair production of top squarks (t), when the lightest neutralino and the top squark have similar masses. At 95% confidence level, wino-like X1/X2 masses are excluded up to 230 GeV for a mass difference of 20 GeV relative to the lightest neutralino. In the higgsino-like model, masses are excluded up to 168 GeV for the same mass difference. For pair production, top squark masses up to 450 GeV are excluded for a mass difference of 40 GeV relative to the lightest neutralino. Physics Letters B, 782 ISSN:0370-2693 ISSN:0031-9163 ISSN:1873-2445

The UN Sustainable Development Goals (SDGs) and the Paris Agreement have ushered in a new era of policymaking to deliver on the formulated goals. Energy policies are key to ensuring universal access to affordable, reliable, sustainable, and modern energy (SDG7). Yet they can also have considerable impact on other goals. To successfully achieve multiple goals concurrently, policies need to balance different objectives and manage their interactions. Refining previously contemplated design principles, we identify three key principles - complementary, transparency and adaptability - as highly pertinent for multiple-objective energy policies based on a synthesis of seventeen coordinated policy case studies. First, policies should entail complementary measures and design provisions that specifically target non-energy objectives (complementarity). Second, policy impacts should be tracked comprehensively in both energy and non-energy domains to uncover diminishing returns and facilitate policy learning (transparency). Third, policies should be capable of adapting to changing objectives over time (adaptability). These principles are rarely considered in current policies, implying the need to mainstream them into the next generation of policymaking by pointing to best practices and new tools.

Polyurethane (PU) possesses excellent thermal properties, making it an ideal material for thermal insulation. Incorporating Phase Change Materials (PCMs) capsules into Polyurethane (PU) has proven to be an effective strategy for enhancing building envelopes. This innovative design substantially enhances indoor thermal stability and minimizes fluctuations in indoor air temperature. To investigate the thermal conductivity of the PU-PCM foam composite, we propose a hierarchical multi-scale model utilizing Physics-Informed Neural Networks (PINNs). This model allows accurate prediction and analysis of the material's thermal conductivity at both the meso-scale and macro-scale. By leveraging the integration of physics-based knowledge and data-driven learning offered by PINNs, we effectively tackle inverse problems and address complex multi-scale phenomena. Furthermore, the obtained thermal conductivity data facilitates the optimization of material design. To fully consider the occupants' thermal comfort within a building envelope, we conduct a case study evaluating the performance of this optimized material in a single room. Simultaneously, we predict the energy consumption associated with this scenario. All outcomes demonstrate the promising nature of this design, enabling passive building energy design and significantly improving occupants' comfort. The successful development of this PINNs-based multi-scale model holds immense potential for advancing our understanding of PU-PCM's thermal properties. It can contribute to the design and optimization of materials for various practical applications, including thermal energy storage systems and insulation design in advanced building envelopes. 25 pages, 25 figures