Abstract: Provided are a high-capacity secondary battery including a cathode including an over-lithiated oxide cathode material or a Ni-rich cathode material; a lithium anode (Li anode); and an electrolyte including a superoxide dismutase mimic catalyst (SODm).

Abstract: Provided is a lithium-air secondary battery including: an air electrode (air cathode); a lithium anode (Li anode); and an electrolyte including a superoxide dismutase mimic catalyst (SODm).

Abstract: Provided are a high-capacity secondary battery including a cathode including an over-lithiated oxide cathode material or a Ni-rich cathode material; a lithium anode (Li anode); and an electrolyte including a superoxide dismutase mimic catalyst (SODm).

Abstract: Provided is a lithium-air secondary battery including: an air electrode (air cathode); a lithium anode (Li anode); and an electrolyte including a superoxide dismutase mimic catalyst (SODm).

Abstract: A system for estimating long term characteristics of a battery includes a learning data input unit for receiving initial characteristic learning data and long term characteristic learning data of a battery to be a learning object; a measurement data input unit for receiving initial characteristic measurement data of a battery to be an object for estimation of long term characteristics; and an artificial neural network operation unit for receiving the initial characteristic learning data and the long term characteristic learning data from the learning data input unit to allow learning of an artificial neural network, receiving the initial characteristic measurement data from the measurement data input unit and applying the learned artificial neural network thereto, and thus calculating long term characteristic estimation data from the initial characteristic measurement data of the battery and outputting the long term characteristic estimation data.

Abstract: A block copolymer for manufacturing a nanowire and a method of manufacturing the same are disclosed. The block copolymer and the method of manufacturing a nanowire using the same are used to fabricate a nanowire having a diameter of less than or equal to 30 nm and a porous nanowire having a diameter within the same range and pores with a diameter of less than or equal to 10 nm.

Abstract: Disclosed are a current collector for a flexible electrode, a method of manufacturing the same, and a negative electrode including the same. The current collector for a flexible electrode includes: a flexible polymer substrate; a cross-linkable polymer layer disposed on the polymer substrate; and a metal layer disposed on the cross-linkable polymer layer, wherein the surface of the cross-linkable polymer layer includes a plurality of protrusions and grooves.

Abstract: Disclosed are an anode active material for a lithium secondary battery, including a material doping and dedoping lithium, and a plurality of external pores having a size of 0.1 to 3 ?m formed in a surface of the material doping and dedoping lithium, the material doping and dedoping lithium including Si, a method of manufacturing the same, and a lithium secondary battery including the same.

Abstract: A method of preparing a silicon nanowire and a method of fabricating a lithium secondary battery including the silicon nanowire are provided. The method of preparing a silicon nanowire may include forming a catalyst layer including metal particles separated from one another on a silicon layer, selectively etching the silicon layer contacting the metal particles, and removing the metal particles.

Abstract: The present invention is directed to a redox-active, conducting polymer energy storage system, said system including an electrode and a counter electrode, wherein the electrode comprises a first conducting polymer and the counter electrode comprises a second conducting polymer, wherein the first conducting polymer is doped by at least one or more first redox-active compounds and/or by a polymer and/or a co-polymer of the one or more first redox-active compounds and the second conducting polymer is doped by at least one or more second redox-active compounds and/or by a polymer and/or a co-polymer of the one or more second redox-active compounds, and wherein there is a potential difference between the dopant for the electrode and the dopant for the counter electrode. In one preferred embodiment, the first or the second redox-active compound is 2,2?-azinobis(3-ethylbenzothiazoline-6-sulfonate) (ABTS).

Abstract: A system includes a learning data input unit for receiving initial and long term characteristic learning data of a battery to be a learning object; a measurement data input unit for receiving initial characteristic measurement data of a battery to be an object for long term characteristic estimation; an artificial neural network operation unit for converting the learning data into first and second data structures, allowing an artificial neural network to learn the learning data based on each data structure, converting the measurement data into first and second data structures, and individually applying the learned artificial neural network corresponding to each data structure to calculate and output long term characteristic estimation data based on each data structure; and a long term characteristic evaluation unit for calculating an error of the estimation data of each data structure and determining reliability of the estimation data depending on error.

Abstract: Disclosed are a current collector for a flexible electrode, a method of manufacturing the same, and a negative electrode including the same. The current collector for a flexible electrode includes: a flexible polymer substrate; a cross-linkable polymer layer disposed on the polymer substrate; and a metal layer disposed on the cross-linkable polymer layer, wherein the surface of the cross-linkable polymer layer includes a plurality of protrusions and grooves.

Abstract: Provided is a positive active material for a lithium rechargeable battery that includes primary particles including a compound being capable of intercalating and deintercalating lithium, and secondary particles including the primary particles gathered with one another, wherein the secondary particles have a void core structure. A method of preparing the same and a lithium rechargeable battery including the same are also provided.

Abstract: A system for estimating long term characteristics of a battery includes a learning data input unit for receiving initial characteristic learning data and long term characteristic learning data of a battery to be a learning object; a measurement data input unit for receiving initial characteristic measurement data of a battery to be an object for estimation of long term characteristics; and an artificial neural network operation unit for receiving the initial characteristic learning data and the long term characteristic learning data from the learning data input unit to allow learning of an artificial neural network, receiving the initial characteristic measurement data from the measurement data input unit and applying the learned artificial neural network thereto, and thus calculating long term characteristic estimation data from the initial characteristic measurement data of the battery and outputting the long term characteristic estimation data.

Abstract: The present invention is directed to a redox-active, conducting polymer energy storage system, said system including an electrode and a counter electrode, wherein the electrode comprises a first conducting polymer and the counter electrode comprises a second conducting polymer, wherein the first conducting polymer is doped by at least one or more first redox-active compounds and/or by a polymer and/or a co-polymer of the one or more first redox-active compounds and the second conducting polymer is doped by at least one or more second redox-active compounds and/or by a polymer and/or a co-polymer of the one or more second redox-active compounds, and wherein there is a potential difference between the dopant for the electrode and the dopant for the counter electrode. In one preferred embodiment, the first or the second redox-active compound is 2,2?-azinobis(3-ethylbenzothiazoline-6-sulfonate) (ABTS).

Abstract: A system includes a learning data input unit for receiving initial and long term characteristic learning data of a battery to be a learning object; a measurement data input unit for receiving initial characteristic measurement data of a battery to be an object for long term characteristic estimation; an artificial neural network operation unit for converting the learning data into first and second data structures, allowing an artificial neural network to learn the learning data based on each data structure, converting the measurement data into first and second data structures, and individually applying the learned artificial neural network corresponding to each data structure to calculate and output long term characteristic estimation data based on each data structure; and a long term characteristic evaluation unit for calculating an error of the estimation data of each data structure and determining reliability of the estimation data depending on error.

Abstract: The present invention provides a monomer comprising the structure: wherein R1 and/or R1? are selected from the group consisting of MeO, EtO, COF3, SO4H, SO3?, SO3H, H, CHNO4S2F3, C5H4N2O6S2F6, C10H10N4S2, CH3, n-Bu, Cl, NH2, EtN, Br, alkyl, ether, ester, sulfonate, ammonium, carboxylate, phosphonate and any combination thereof, R2 and/or R2?, are selected from the group consisting of EtO, SO3H, H, C10H10N4S2, CH3, Cl, C6H14N2S and any combination thereof, R3 and/or R3? are selected from the group consisting of CH3, Cl, H and any combination thereof, and R4 and/or R4? are selected from the group consisting of CH3, H, C2H5, C4H9, C6H5, C8H17, C2H5S, C3H7S, C4H8Br, C10H23N, C20H21N2, C18H25N2, C21H23N2, C31H29N2O2, C22H25N4, C20H25N2, C3H7OS, and any combination thereof.

Abstract: The present invention provides a monomer comprising the structure: wherein R1 and/or R1? are selected from the group consisting of MeO, EtO, COF3, SO4H, SO3?, SO3H, H, CHNO4S2F3, C5H4N2O6S2F6, C10H10N4S2, CH3, n-Bu, Cl, NH2, EtN, Br, alkyl, ether, ester, sulfonate, ammonium, carboxylate, phosphonate and any combination thereof, R2 and/or R2?, are selected from the group consisting of EtO, SO3H, H, C10H10N4S2, CH3, Cl, C6H14N2S and any combination thereof, R3 and/or R3? are selected from the group consisting of CH3, Cl, H and any combination thereof, and R4 and/or R4? are selected from the group consisting of CH3, H, C2H5, C4H9, C6H5, C8H17, C2H5S, C3H7S, C4H8Br, C10H23N, C20H21N2, C18H25N2, C21H23N2, C31H29N2O2, C22H25N4, C20H25N2, C3H7OS, and any combination thereof.