Abstract: A cathode electrode assembly is disclosed, the cathode electrode assembly comprising an active material, a current collector, a conductive additive substance, and a polynorbornene-based (PNB) polymer binder configured to bind the active material and the conductive additive substance and maintain electrical contact between the active material and the conductive additive substance with the current collector. An alternative cathode electrode assembly comprising active material, a current collector, a conductive additive substance, a PNB polymer binder, and at least one polyacrylic acid (PAA) side chain configured to interface with the PNB polymer binder is also disclosed. A functional group is further disclosed, the functional group being configured to interface with a binder in a cathode electrode assembly of an electric battery system, the functional group comprising at least one PAA side chain.

Abstract: Disclosed is an engineered coating material decorated on a conductive carbon, comprising: a coating material disposed on a surface of the conductive carbon with a partial coverage or full coverage, wherein the coating material comprises at least one material selected from a group comprising: AlOx, TiOx, SnOx, ZnOx, NbOx, TiNbxOy, AlPxOy, MgOx, LiNbxOy, BOx, CeOx, LiAlxOy, Sn(PO4)x, ZrOx, MgAlxOy, SiOx, NiOx, Pt, Pd, Ir, RuxOy, CeZrxOy, BiOx, TiNx, ZnO, ZnS, MnO2, NbO2, VS2, TiS2, CoS2, and Al2O3.

Abstract: Described are optical devices, such as photovoltaic modules that include features such as solar tracking, solar concentration, tandem cell arrangements, and thermal management to achieve high photovoltaic efficiency. The photovoltaic modules can be constructed using a variety of different materials and configurations or as a monoblock functionally graded structure to limit manufacturing, operational, and maintenance complexities while achieving high performance. The use of thermal management structures directly integrated into a photovoltaic cell can maintain such cells at desirable temperatures, which can be beneficial in optical concentrating configurations.

Abstract: Disclosed is an engineered coating material decorated on a conductive carbon, comprising: a coating material disposed on a surface of the conductive carbon with a partial coverage or full coverage, wherein the coating material comprises at least one material selected from a group comprising: AlOx, TiOx, SnOx, ZnOx, NbOx, TiNbxOy, AlPxOy, MgOx, LiNbxOy, BOx, CeOx, LiAlxOy, Sn(PO4)x, ZrOx, MgAlxOy, SiOx, NiOx, Pt, Pd, Ir, RuxOy, CeZrxOy, BiOx, TiNx, ZnO, ZnS, MnO2, NbO2, VS2, TiS2, CoS2, and Al2O3.

Abstract: Disclosed herein is a sensors-as-a-service ecosystem. In use, the system includes functions for receiving first sensor data at a sensors as a service platform, where the first sensor data corresponds to a first level of capabilities for a first sensor. The system also receives a selection of a sensor upgrade for the first sensor and provisions enhanced sensor capabilities for the sensor upgrade based on the selection. Furthermore, the system sends a sensor update with the enhanced sensor capabilities from the sensors as a service platform to the first sensor. Finally, the system receives second sensor data from the first sensor at the sensors as a service platform, where the second sensor data corresponds to a second level of capabilities for the first sensor.

Abstract: Methods and system to learn precise sensing fingerprints based on machine learning integration are disclosed herein. In use, the system receives at least one first parameter associated with at least one sensor and associates the first parameter with a pre-identified first digital signature in a signature database. A machine learning system is trained based on the first parameter and the pre-identified digital signature. The system then receives at least one second parameter from the at least one sensor and determines that the second parameter is independent of a digital signature in the signature database. Using the machine learning system, a second digital signature for the second parameter is identified and saved in the signature database.

Abstract: Disclosed herein is a sensors-as-a-service ecosystem. In use, the system includes functions for receiving first sensor data at a sensors as a service platform, where the first sensor data corresponds to a first level of capabilities for a first sensor. The system also receives a selection of a sensor upgrade for the first sensor and provisions enhanced sensor capabilities for the sensor upgrade based on the selection. Furthermore, the system sends a sensor update with the enhanced sensor capabilities from the sensors as a service platform to the first sensor. Finally, the system receives second sensor data from the first sensor at the sensors as a service platform, where the second sensor data corresponds to a second level of capabilities for the first sensor.

Abstract: Disclosed herein is a sensors-as-a-service ecosystem. In use, the system includes functions for receiving first sensor data at a sensors as a service platform, where the first sensor data corresponds to a first level of capabilities for a first sensor. The system also receives a selection of a sensor upgrade for the first sensor and provisions enhanced sensor capabilities for the sensor upgrade based on the selection. Furthermore, the system sends a sensor update with the enhanced sensor capabilities from the sensors as a service platform to the first sensor. Finally, the system receives second sensor data from the first sensor at the sensors as a service platform, where the second sensor data corresponds to a second level of capabilities for the first sensor.

Abstract: Disclosed is an engineered coating material decorated on a conductive carbon, comprising: a coating material disposed on a surface of the conductive carbon with a partial coverage or full coverage, wherein the coating material comprises at least one material selected from a group comprising: AlOx, TiOx, SnOx, ZnOx, NbOx, TiNbxOy, AlPxOy, MgOx, LiNbxOy, BOx, CeOx, LiAlxOy, Sn(PO4)x, ZrOx, MgAlxOy, SiOx, NiOx, Pt, Pd, Ir, RuxOy, CeZrxOy, BiOx, TiNx, ZnO, ZnS, MnO2, NbO2, VS2, TiS2, CoS2, and Al2O3.

Abstract: A battery-powered analyte sensing system includes a printed battery and an analyte sensor. The printed battery includes an anode composed of a non-toxic biocompatible metal, a first carbon-based current collector in electrical contact with the anode, a three-dimensional hierarchical mesoporous carbon-based cathode, a second carbon-based current collector, and an electrolyte layer disposed between the anode and the cathode, the electrolyte layer configured to activate the printed battery when the electrolyte is released into one or both the anode and the cathode. The analyte sensor includes a sensing material and a reactive chemistry additive in the sensing material.

Abstract: A battery-powered analyte sensing system includes a printed battery and an analyte sensor. The printed battery includes an anode composed of a non-toxic biocompatible metal, a first carbon-based current collector in electrical contact with the anode, a three-dimensional hierarchical mesoporous carbon-based cathode, a second carbon-based current collector, and an electrolyte layer disposed between the anode and the cathode, the electrolyte layer configured to activate the printed battery when the electrolyte is released into one or both the anode and the cathode. The analyte sensor includes a sensing material and a reactive chemistry additive in the sensing material.

Abstract: Methods and system to learn precise sensing fingerprints based on machine learning integration are disclosed herein. In use, the system receives at least one first parameter associated with at least one sensor and associates the first parameter with a pre-identified first digital signature in a signature database. A machine learning system is trained based on the first parameter and the pre-identified digital signature. The system then receives at least one second parameter from the at least one sensor and determines that the second parameter is independent of a digital signature in the signature database. Using the machine learning system, a second digital signature for the second parameter is identified and saved in the signature database.

Abstract: Methods and system to learn precise sensing fingerprints based on machine learning integration are disclosed herein. In use, the system receives at least one first parameter associated with at least one sensor and associates the first parameter with a pre-identified first digital signature in a signature database. A machine learning system is trained based on the first parameter and the pre-identified digital signature. The system then receives at least one second parameter from the at least one sensor and determines that the second parameter is independent of a digital signature in the signature database. Using the machine learning system, a second digital signature for the second parameter is identified and saved in the signature database.

Abstract: Methods and system to learn precise sensing fingerprints based on machine learning integration are disclosed herein. In use, the system receives at least one first parameter associated with at least one sensor and associates the first parameter with a pre-identified first digital signature in a signature database. A machine learning system is trained based on the first parameter and the pre-identified digital signature. The system then receives at least one second parameter from the at least one sensor and determines that the second parameter is independent of a digital signature in the signature database. Using the machine learning system, a second digital signature for the second parameter is identified and saved in the signature database.

Abstract: Methods and system to learn precise sensing fingerprints based on machine learning integration are disclosed herein. In use, the system receives at least one first parameter associated with at least one sensor and associates the first parameter with a pre-identified first digital signature in a signature database. A machine learning system is trained based on the first parameter and the pre-identified digital signature. The system then receives at least one second parameter from the at least one sensor and determines that the second parameter is independent of a digital signature in the signature database. Using the machine learning system, a second digital signature for the second parameter is identified and saved in the signature database.

Abstract: Methods and system to learn precise sensing fingerprints based on machine learning integration are disclosed herein. In use, the system receives at least one first parameter associated with at least one sensor and associates the first parameter with a pre-identified first digital signature in a signature database. A machine learning system is trained based on the first parameter and the pre-identified digital signature. The system then receives at least one second parameter from the at least one sensor and determines that the second parameter is independent of a digital signature in the signature database. Using the machine learning system, a second digital signature for the second parameter is identified and saved in the signature database.

Abstract: Described are optical devices, such as photovoltaic modules that include features such as solar tracking, solar concentration, tandem cell arrangements, and thermal management to achieve high photovoltaic efficiency. The photovoltaic modules can be constructed using a variety of different materials and configurations or as a monoblock functionally graded structure to limit manufacturing, operational, and maintenance complexities while achieving high performance. The use of thermal management structures directly integrated into a photovoltaic cell can maintain such cells at desirable temperatures, which can be beneficial in optical concentrating configurations.

Abstract: Described are optical devices, such as photovoltaic modules that include features such as solar tracking, solar concentration, tandem cell arrangements, and thermal management to achieve high photovoltaic efficiency. The photovoltaic modules can be constructed using a variety of different materials and configurations or as a monoblock functionally graded structure to limit manufacturing, operational, and maintenance complexities while achieving high performance. The use of thermal management structures directly integrated into a photovoltaic cell can maintain such cells at desirable temperatures, which can be beneficial in optical concentrating configurations.

Abstract: A container for storing one or more items is disclosed. The container may include a surface defining a volume of the container and a label printed on the container. In various implementations, the label includes a substrate, a plurality of carbon-based sensors printed on the substrate, and one or more electrodes printed on the substrate. The sensors may be collectively configured to detect a presence of one or more analytes within the container. Each sensor may be configured to react with a unique group of analytes in response to an electromagnetic signal received from an external device. The electrodes may be configured to provide one or more output signals indicating the presence or absence of the one or more analytes within the container.

Abstract: A solid-state battery comprising at least one electrode stack that includes a solid-state electrolyte, cathode, and optionally an anode. The electrolyte can be an oxygen-free and carbon-free solid-state and alkali-conducting electrolyte that is processable in oxygen-containing atmospheres with room temperature ionic conductivity greater than 1 mS/cm and room temperature shear modulus greater between 1 GPa and 20 GPa. The cathode can be composed of an electrochemically-active material from Group 16 of the periodic table having a high surface area greater than 10 m2/g and contact with a conductive carbon material. The anode can be comprised of any material that can reversibly accommodate group 1 or group 2 elements or the base group 1 or group 2 element. The solid-state battery can utilize a solid-state electrolyte having a lithium-conducting sulfide electrolyte, of the formula U6PS5X (X=Cl, Br, I) with argyrodite structure and exhibiting ionic conductivity over 1 mS cm-1 at room temperature.