Derivative works — US Patent 11515528

Defensive Disclosure: US Patent 11515528

This document serves as a defensive disclosure aimed at broadening the prior art landscape surrounding US Patent 11515528, titled "Electrodes, lithium-ion batteries, and methods of making and using same." The objective is to describe various derivative works and technical variations of the patented invention, thereby rendering future incremental improvements by competitors "obvious" or "non-novel" under 35 U.S.C. § 103 and § 102, respectively. The disclosures focus on material and component substitution, operational parameter expansion, cross-domain application, integration with emerging technologies, and inverse/failure mode designs, providing enabling descriptions and visual illustrations.

Derivatives of Core Claim 1: Anode Composition

Core Claim 1 (Anode Composition Overview): An anode comprising a porous composite including agglomerated nanocomposites, each having a dendritic particle (3D, randomly-ordered assembly of electrically conducting nanoparticles) and discrete non-porous nanoparticles of a non-carbon Group 4A element disposed on its surface, with electrical communication between dendritic particles.

Derivative 1.1: Material & Component Substitution - Metallic Dendritic Core with Germanium Nanoparticles

Enabling Description:
A porous composite anode is disclosed wherein the electrically conducting dendritic particle is formed from a three-dimensional, randomly-ordered assembly of annealed nickel (Ni) nanowires, each having an average diameter of 10-50 nm and a length of 500 nm to 5 µm. These Ni nanowires are sintered together at 800°C under a reducing atmosphere (e.g., 5% H₂ in Ar) to form the dendritic structure. Disposed on the surface of these Ni dendritic particles are a plurality of discrete, non-porous germanium (Ge) nanoparticles, with an average longest dimension of 20-150 nm. The Ge nanoparticles are deposited via atomic layer deposition (ALD) using germane (GeH₄) as a precursor, cycling at 150°C to ensure discrete, non-porous growth. The Ni dendritic particles are in direct electrical communication. The nanocomposites are agglomerated into a porous composite using a polyimide binder, subsequently carbonized at 900°C under argon, to further enhance electrical conductivity and mechanical stability. The total pore volume within the porous composite is maintained at approximately 2.5 times the volume occupied by all Ge nanoparticles.

graph TD
    A[Ni Nanowires] -- Sintering (800C, H2/Ar) --> B(Ni Dendritic Particle)
    B -- ALD (GeH4, 150C) --> C{Nanocomposite: Ni Dendrite + Discrete Ge NPs}
    C -- Agglomeration (Polyimide Binder) --> D[Agglomerated Nanocomposites]
    D -- Carbonization (900C, Ar) --> E(Porous Composite Anode)
    E -- Electrical Communication --> E

Derivative 1.2: Operational Parameter Expansion - Ultra-Thin Film Anode for Micro-Batteries

Enabling Description:
An anode designed for ultra-thin film micro-batteries is disclosed, comprising a porous composite formed as a film with a thickness of 5-20 µm. The agglomerated nanocomposites are formed wherein the electrically conducting dendritic particles are derived from pyrolyzed polyacrylonitrile (PAN) nanofibers (100-300 nm diameter) forming a carbonaceous dendritic network. Discrete non-porous silicon-tin (Si₀.₈Sn₀.₂) alloy nanoparticles, with an average longest dimension of 5-20 nm, are deposited on the carbon nanofiber dendritic particles using co-sputtering of Si and Sn targets. The entire composite film is fabricated with a controlled porosity ranging from 60-85% to accommodate extreme volume changes during rapid charge/discharge cycles (up to 50C rate). The film operates reliably at temperatures down to -40°C, achieved by optimizing the electrolyte-interface and ensuring robust electrical pathways within the dense, yet porous, carbon network. The electrical communication between dendritic particles is enhanced by post-deposition calendering.

graph TD
    A[PAN Nanofibers] -- Pyrolysis --> B(Carbon Dendritic Particle)
    B -- Co-Sputtering (Si/Sn) --> C{Nanocomposite: C Dendrite + Discrete SiSn NPs}
    C -- Film Formation & Agglomeration --> D[Ultra-Thin Porous Composite Film Anode]
    D -- Operates at -40C / 50C rate --> E(Micro-Battery Application)

Derivative 1.3: Cross-Domain Application - Bio-Integrated Catalyst Support

Enabling Description:
A porous composite is disclosed for use as a bio-integrated catalyst support in biochemical reactors. The nanocomposites feature dendritic particles formed from a three-dimensional, randomly-ordered assembly of electrically conductive polyaniline (PANI) nanofibers, synthesized via oxidative polymerization and having diameters of 50-200 nm. Disposed on the surface of these PANI dendritic particles are a plurality of discrete, non-porous gold (Au) nanoparticles, with an average longest dimension of 10-40 nm, applied via electroless deposition using HAuCl₄. The PANI dendritic particles facilitate electron transfer within the composite, and the discrete Au nanoparticles serve as catalytic sites for enzymatic reactions (e.g., glucose oxidation). The agglomerated nanocomposites form a porous matrix, allowing for efficient substrate diffusion and product removal in aqueous biological environments. Electrical communication between PANI dendritic particles provides an electron collection network for bio-electrocatalysis.

graph TD
    A[Aniline Monomers] -- Oxidative Polymerization --> B(PANI Nanofibers)
    B -- Self-Assembly --> C(PANI Dendritic Particle)
    C -- Electroless Deposition (HAuCl4) --> D{Nanocomposite: PANI Dendrite + Discrete Au NPs}
    D -- Agglomeration --> E[Porous Bio-Catalyst Support]
    E -- Substrate Diffusion & Electron Flow --> F(Biochemical Reactor)

Derivative 1.4: Integration with Emerging Tech - AI-Optimized Smart Anode

Enabling Description:
A smart anode system for lithium-ion batteries is disclosed, integrating AI-driven optimization, IoT sensors, and blockchain technology. The anode consists of a porous composite with agglomerated nanocomposites. The dendritic particles are a 3D network of carbon nanotubes (CNTs), self-assembled and inter-grown via chemical vapor deposition. Discrete, non-porous silicon nanoparticles are disposed on the CNT surfaces. Integrated IoT sensors (e.g., thin-film thermocouples, strain gauges) are embedded within the porous composite during agglomeration to monitor internal temperature and volume expansion in real-time. Data from these sensors is fed into an AI-driven Battery Management System (BMS) that dynamically adjusts charge/discharge protocols to optimize cycle life and safety based on predicted degradation, specific to the anode's real-time state. Furthermore, the entire manufacturing process and material provenance (from raw material suppliers to final anode assembly) are recorded and verified using a blockchain ledger, ensuring transparency and quality control. The AI optimizes the initial morphological parameters (e.g., CNT density, Si nanoparticle size distribution) during synthesis via feedback loops.

graph LR
    A[Raw Materials] -- Provenance (Blockchain) --> B(CNT Synthesis)
    B -- AI-Optimized Growth --> C(CNT Dendritic Particle)
    C -- Si Nanoparticle Deposition --> D{Nanocomposite: CNT Dendrite + Discrete Si NPs}
    D -- Agglomeration + IoT Sensors --> E[Smart Porous Composite Anode]
    E -- Real-time Data (IoT) --> F(AI-driven BMS)
    F -- Dynamic Control --> E

Derivative 1.5: The "Inverse" or Failure Mode - Self-Healing, Low-Capacity Anode

Enabling Description:
A low-capacity, self-healing anode is disclosed, designed for extended shelf-life and safe failure modes in long-duration, low-power applications (e.g., remote IoT sensors). The anode comprises a porous composite of agglomerated nanocomposites. The dendritic particles are formed from a conductive polymer matrix (e.g., polypyrrole, PPy) with lower electrical conductivity than carbon, structured as a 3D randomly-ordered network. Discrete, non-porous silicon-oxycarbide (SiOC) nanoparticles, exhibiting lower theoretical capacity and reduced volume expansion compared to pure Si, are disposed on the PPy dendritic particle surfaces. These SiOC nanoparticles are designed with a thin, self-healing polymer shell (e.g., microencapsulated with reversible Diels-Alder adducts) that can repair minor cracks or electrolyte decomposition layers upon thermal cycling, extending capacity retention in limited-functionality mode. Upon reaching a critical overcharge voltage, embedded micro-resistors within the composite trigger a localized exothermic reaction in specific, sacrificial regions of the PPy dendritic network, inducing a controlled increase in internal resistance and safe, gradual capacity fade, rather than catastrophic thermal runaway or short circuit.

stateDiagram
    [*] --> Idle
    Idle --> Charging: Li-ion Insertion
    Charging --> Discharging: Li-ion Extraction
    Charging --> Overcharge[Overcharge Detected]: Voltage Exceeds Threshold
    Overcharge --> FailSafe[Trigger Fail-Safe]: Activate Micro-Resistors
    FailSafe --> IncResistance[Increase Internal Resistance]: Localized Exothermic Reaction
    IncResistance --> CapacityFade[Gradual Capacity Fade]: Reduced Functionality
    CapacityFade --> Off[*]
    Idle --> Degradation: Micro-cracks
    Degradation --> SelfHeal: Thermal Cycling / Reversible Polymer
    SelfHeal --> Idle

Derivatives of Core Claim 2: Granular Anode Composition

Core Claim 2 (Granular Anode Composition Overview): An anode comprising a matrix of spherical or substantially-spherical porous composite granules. Each granule includes agglomerated nanocomposites, where dendritic particles are formed from annealed carbon black nanoparticles, and discrete non-porous silicon nanoparticles are disposed on their surface, with electrical communication.

Derivative 2.1: Material & Component Substitution - Graphitic Nanofiber Dendrites in Granules with Tin Nanoparticles

Enabling Description:
An anode comprises a matrix of spherical porous composite granules, where each granule (average diameter 20-40 µm) is formed from agglomerated nanocomposites. Within these nanocomposites, the dendritic particles are formed from a three-dimensional, randomly-ordered assembly of vapor-grown carbon nanofibers (VGCNFs) with diameters of 50-150 nm, annealed at 2500°C for graphitization and interconnection. Discrete, non-porous tin (Sn) nanoparticles, with an average longest dimension of 50-250 nm, are deposited on the surface of the graphitic VGCNF dendritic particles via thermal evaporation in a low-pressure environment. The VGCNF dendritic particles within each nanocomposite, and between adjacent nanocomposites, are in electrical communication due to their highly graphitized and interconnected nature. The granules are formed by spray drying a suspension of the nanocomposites with a lignosulfonate binder, followed by pyrolysis at 800°C to carbonize the binder and further stabilize the granular structure.

graph TD
    A[Vapor Grown Carbon Nanofibers] -- Annealing (2500C) --> B(Graphitic VGCNF Dendritic Particle)
    B -- Thermal Evaporation (Sn) --> C{Nanocomposite: VGCNF Dendrite + Discrete Sn NPs}
    C -- Spray Drying (Lignosulfonate Binder) --> D[Spherical Granule Precursor]
    D -- Pyrolysis (800C) --> E(Porous Composite Granule)
    E -- Matrix Formation --> F(Anode Matrix)

Derivative 2.2: Operational Parameter Expansion - High-Temperature Granules for Solid-State Batteries

Enabling Description:
A granular anode composition for solid-state lithium-ion batteries is disclosed, specifically engineered for operational temperatures ranging from 80°C to 150°C. The spherical porous composite granules (average diameter 10-30 µm) are composed of agglomerated nanocomposites. The dendritic particles are formed from highly crystalline boron-doped graphitic carbon black nanoparticles, annealed at 2200°C to ensure enhanced thermal stability and conductivity. Discrete, non-porous lithium-silicon (LiₓSi) alloy nanoparticles (Li₂Si₅, average longest dimension 10-50 nm) are pre-lithiated and then directly disposed on the surface of the boron-doped carbon dendritic particles via a reactive deposition process using silane and lithium vapor at 400°C. The granules are then coated with a thin, thermally stable solid-state electrolyte interface (SEI) layer (e.g., LiPON or Li₂S-P₂S₅) via ALD to ensure stable operation at elevated temperatures and interface compatibility with solid electrolytes. The agglomeration process involves hot pressing the nanocomposites, followed by sintering at 700°C in an inert atmosphere, yielding high-density, thermally robust granules.

graph TD
    A[Boron-Doped Carbon Black] -- Annealing (2200C) --> B(Crystalline B-C Dendritic Particle)
    B -- Reactive Deposition (SiH4/Li vapor, 400C) --> C{Nanocomposite: B-C Dendrite + Discrete LiSi NPs}
    C -- Hot Pressing & Sintering (700C) --> D[High-Temp Porous Granule]
    D -- ALD (LiPON/LiSPS) --> E(SEI-Coated Granule for Solid-State)
    E -- Matrix Formation --> F(Solid-State Anode)

Derivative 2.3: Cross-Domain Application - Granular Hydrogen Storage Media

Enabling Description:
Porous composite granules are disclosed for use as reversible hydrogen storage media. The spherical granules (average diameter 50-100 µm) comprise agglomerated nanocomposites where the dendritic particles are formed from an interconnected network of porous graphene nanosheets, synthesized by chemical vapor deposition on a templating agent that is later removed. Discrete, non-porous magnesium hydride (MgH₂) nanoparticles, with an average longest dimension of 5-30 nm, are infiltrated and deposited onto the surface of the graphene dendritic particles using a solution-based synthesis route followed by thermal desorption. The graphene network acts as a highly conductive scaffold to facilitate heat transfer during hydrogen adsorption/desorption cycles, overcoming the slow kinetics of bulk MgH₂. Electrical communication throughout the graphene dendritic particles aids in electrochemical promotion of catalysis for hydrogen uptake/release. The porous nature of the granules provides ample surface area and interstitial volume for high hydrogen storage capacity.

graph TD
    A[Graphene Nanosheets] -- Interconnection --> B(Porous Graphene Dendritic Particle)
    B -- Infiltration & Deposition (MgH2) --> C{Nanocomposite: Graphene Dendrite + Discrete MgH2 NPs}
    C -- Agglomeration --> D[Porous Composite Granule]
    D -- Heat Transfer & Hydrogen Flow --> E(Hydrogen Storage Reactor)

Derivative 2.4: Integration with Emerging Tech - Granule-Based Self-Assembling Anode

Enabling Description:
A self-assembling anode system is disclosed, utilizing spherical porous composite granules (average diameter 15-30 µm) designed for robotic placement and in-situ characterization. Each granule contains agglomerated nanocomposites with dendritic particles of annealed carbon black and discrete non-porous silicon nanoparticles, as per the original patent. However, these granules are surface-functionalized with specific ligands or magnetic tags (e.g., iron oxide nanoparticles encapsulated in a polymer) that enable automated, spatially-controlled self-assembly into a defined electrode matrix using robotic manipulators or magnetic fields. Each granule incorporates a passive RFID tag containing a unique identifier and batch manufacturing data, allowing for individual granule tracking via IoT readers during assembly and throughout the battery's lifecycle. Post-assembly, real-time impedance spectroscopy data collected from the electrode is processed by a machine learning algorithm to verify optimal electrical communication between granules and identify any assembly defects, ensuring homogeneous performance.

sequenceDiagram
    Robotic Arm -> Granule 1: Pick & Place (Ligand/Magnetic)
    Granule 1 -> Granule 2: Self-Assembly Trigger
    Granule 2 -> Granule 3: Self-Assembly Trigger
    Granule 3 -> IoT Reader: RFID Scan (Batch Data)
    IoT Reader -> ML Algorithm: Impedance Data
    ML Algorithm -> Robotic Arm: Feedback (Assembly Correction)
    Note over ML Algorithm: Verify Electrical Communication

Derivative 2.5: The "Inverse" or Failure Mode - Environmentally Responsive Degradation Granules

Enabling Description:
Porous composite granules are disclosed, engineered for controlled, environmentally responsive degradation post-lifecycle, minimizing hazardous waste. Each spherical granule (average diameter 25-50 µm) is built upon annealed carbon black dendritic particles decorated with discrete, non-porous silicon nanoparticles. The binding matrix for agglomeration is a bio-degradable polymer (e.g., polylactic acid, PLA) that is carbonized only superficially to provide initial mechanical integrity and electrical pathways, leaving the core PLA intact. Upon exposure to specific environmental conditions (e.g., high humidity, microbial activity, or a specific pH solution) post-disposal, the internal PLA binder fully degrades, causing the granules to disintegrate into their constituent, less harmful, nano-components. This controlled disintegration prevents large-scale mechanical integrity issues associated with spent batteries while facilitating easier separation and recycling of silicon and carbon, operating as a "degradation-on-demand" system rather than a long-lasting, robust structure.

stateDiagram
    [*] --> ActiveUse
    ActiveUse --> Disposal
    Disposal --> EnvironmentalExposure[Environmental Exposure (Humidity/Microbes/pH)]
    EnvironmentalExposure --> PLADegradation[PLA Binder Degradation]
    PLADegradation --> GranuleDisintegration[Granule Disintegration]
    GranuleDisintegration --> MaterialSeparation[Facilitated Material Separation]
    MaterialSeparation --> Recycling[*]

Derivatives of Core Claim 4: Method of Making an Anode

Core Claim 4 (Method of Making Overview): A method including forming a 3D, randomly-ordered dendritic particle from discrete nanoparticles of an electrically conducting material; disposing discrete non-porous nanoparticles of a non-carbon Group 4A element on its surface to form a nanocomposite particle; and assembling these nanocomposite particles to form a bulk unitary body or granule, ensuring electrical communication.

Derivative 4.1: Material & Component Substitution - Template-Assisted Formation of Metal Oxide Dendrites

Enabling Description:
A method of making an anode involves:

Forming Dendritic Particle: Utilizing a template-assisted hydrothermal synthesis to grow three-dimensional, randomly-ordered dendritic particles from discrete titanium dioxide (TiO₂) nanoparticles. This involves a sol-gel process within a porous alumina template, followed by template removal, yielding a mesoporous TiO₂ dendritic network.
Disposing Non-Carbon Group 4A Nanoparticles: Introducing discrete, non-porous lead (Pb) nanoparticles, with an average longest dimension of 30-100 nm, onto the surface of the TiO₂ dendritic particles via galvanic displacement using lead acetate solution. The TiO₂ acts as a scaffold, and the Pb nanoparticles are selectively grown.
Assembling Nanocomposite Particles: The resulting TiO₂-Pb nanocomposite particles are then assembled into a bulk unitary body via electrophoretic deposition (EPD) onto a current collector. The EPD parameters (voltage, time, solvent) are controlled to ensure sufficient packing and electrical communication between the TiO₂ dendritic frameworks of adjacent nanocomposites, followed by annealing at 400°C under vacuum to enhance inter-particle connectivity and partially reduce TiO₂ for improved electronic conductivity.

graph TD
    A[Porous Alumina Template + TiO2 Precursors] -- Hydrothermal Synthesis & Template Removal --> B(TiO2 Dendritic Particle)
    B -- Galvanic Displacement (Lead Acetate) --> C{Nanocomposite: TiO2 Dendrite + Discrete Pb NPs}
    C -- Electrophoretic Deposition (EPD) --> D[Bulk Unitary Anode Body]
    D -- Annealing (400C, Vacuum) --> E(Enhanced Electrical Communication)

Derivative 4.2: Operational Parameter Expansion - Continuous In-Situ Plasma Synthesis

Enabling Description:
A method for high-throughput continuous manufacturing of an anode involves:

Forming Dendritic Particle: Utilizing a continuous flow atmospheric-pressure plasma reactor to form three-dimensional, randomly-ordered dendritic particles. Carbon precursors (e.g., methane/argon mixture) are injected into a plasma jet, causing rapid nucleation and growth of carbon nanoparticles (10-50 nm), which immediately fuse into dendritic structures due to high collision frequency and temperature gradients within the plasma plume.
Disposing Non-Carbon Group 4A Nanoparticles: In-situ introduction of silane gas downstream in the same plasma reactor. The silane undergoes rapid decomposition and deposition, forming discrete, non-porous silicon nanoparticles (15-80 nm) directly on the still-forming carbon dendritic particles. The short residence time and precise control of gas mixing prevent continuous film formation.
Assembling Nanocomposite Particles: The nascent nanocomposite particles, still entrained in the gas flow, are immediately directed into a turbulent flow agglomeration chamber. A solvent spray (e.g., ethanol) with a polymeric binder (e.g., polyimide precursor) induces wet agglomeration into spherical granules (20-50 µm). These granules are then collected, dried, and heat-treated in a continuous furnace at 900°C under inert gas to pyrolyze the binder to carbon, ensuring robust electrical communication within and between the granules.

flowchart TD
    A[Methane/Argon Plasma Precursors] -- Plasma Jet --> B(Carbon Dendritic Particle Growth)
    B -- Silane Injection (In-Situ) --> C(Si Nanoparticle Deposition)
    C -- Turbulent Flow Agglomeration (Solvent + Binder) --> D(Wet Granule Formation)
    D -- Continuous Furnace (Drying & Pyrolysis) --> E(Porous Composite Granules)
    E -- Collection --> F(Anode Material)

Derivative 4.3: Cross-Domain Application - Fabricating Biosensor Scaffolds with Quantum Dots

Enabling Description:
A method is disclosed for fabricating biosensor scaffolds:

Forming Dendritic Particle: Forming a three-dimensional, randomly-ordered dendritic particle from discrete nanoparticles of a biocompatible, electrically conducting polymer, such as poly(3,4-ethylenedioxythiophene) (PEDOT) nanoparticles (average diameter 20-80 nm). This is achieved via electrospinning of a PEDOT solution followed by solvent evaporation to form the entangled network.
Disposing Non-Carbon Group 4A Nanoparticles: Disposing a plurality of discrete, non-porous cadmium selenide (CdSe) quantum dots (average longest dimension 2-8 nm, acting as "non-carbon Group 4A element" analogs for quantum sensing) on the surface of the PEDOT dendritic particle. This is performed via a directed self-assembly technique, where the CdSe QDs are functionalized with thiol groups to selectively bind to the PEDOT surface.
Assembling Nanocomposite Particles: The resulting PEDOT-CdSe nanocomposite particles are assembled to form a porous bulk unitary body or micro-patterned scaffold using 3D bioprinting techniques, where a bio-ink containing the nanocomposites is extruded layer-by-layer. The printing parameters ensure adequate electrical communication pathways between the PEDOT dendritic particles, creating a scaffold for specific analyte detection through changes in quantum dot fluorescence or electrical impedance.

graph TD
    A[PEDOT Solution] -- Electrospinning & Solvent Evaporation --> B(PEDOT Dendritic Particle)
    B -- Functionalized CdSe QD Self-Assembly --> C{Nanocomposite: PEDOT Dendrite + Discrete CdSe QDs}
    C -- 3D Bioprinting --> D[Porous Biosensor Scaffold]
    D -- Analyte Detection --> E(Biosensor Application)

Derivative 4.4: Integration with Emerging Tech - Digital Twin Guided Manufacturing

Enabling Description:
A method of making an anode employing a digital twin for real-time process optimization is disclosed:

Forming Dendritic Particle: A three-dimensional, randomly-ordered dendritic particle is formed from a plurality of discrete nanoparticles of an electrically conducting material (e.g., graphitized multi-walled carbon nanotubes). The growth parameters (temperature, gas flow rates, catalyst concentration) in a fluidized bed CVD reactor are continuously monitored by IoT sensors and input into a digital twin simulation. The digital twin, an AI model, predicts the dendritic morphology and electrical conductivity based on these parameters.
Disposing Non-Carbon Group 4A Nanoparticles: Discrete, non-porous silicon nanoparticles are disposed on the surface of the dendritic particle via chemical vapor deposition. The silane precursor flow rate, temperature, and reaction time are adjusted in real-time by the AI-driven digital twin to achieve a target nanoparticle size distribution and coverage, minimizing aggregation and ensuring non-porosity, based on predictive modeling.
Assembling Nanocomposite Particles: The resulting nanocomposite particles are assembled into spherical granules using a wet granulation process. The AI-driven digital twin monitors granule size distribution and moisture content using in-line vision systems and adjusts binder spray rates and agitator speed to maintain optimal granule characteristics. Electrical communication between dendritic particles within the agglomerated granules is verified in-situ via electrical impedance tomography, with feedback provided to the digital twin for closed-loop control of the granulation process.

graph TD
    A[MWCNT Precursors + CVD] -- IoT Sensors --> B(Digital Twin)
    B -- AI Optimization --> C(Dendritic Particle Formation)
    C -- Silane CVD + AI Optimization --> D(Si Nanoparticle Disposition)
    D -- Wet Granulation + IoT/Vision --> E(Nanocomposite Particle Assembly into Granules)
    E -- EIT Feedback --> B
    F(Anode Material)
    E --> F

Derivative 4.5: The "Inverse" or Failure Mode - Reversible De-Aggregation for End-of-Life Recycling

Enabling Description:
A method for making an anode with integrated end-of-life de-aggregation functionality for enhanced recycling is disclosed:

Forming Dendritic Particle: Three-dimensional, randomly-ordered dendritic particles are formed from discrete nanoparticles of highly purified graphitic carbon black, annealed at 2000°C.
Disposing Non-Carbon Group 4A Nanoparticles: Discrete, non-porous silicon nanoparticles are disposed on the carbon black dendritic particles via plasma-enhanced chemical vapor deposition (PECVD).
Assembling Nanocomposite Particles: A crucial step involves assembling the nanocomposite particles using a sacrificial, pH-sensitive, polymeric binder (e.g., poly(lactic-co-glycolic acid) PLGA or chitosan) that maintains electrical communication but is designed to rapidly degrade and release its binding capacity under specific acidic or basic conditions. The assembly forms spherical granules via wet granulation. The electrical communication between dendritic particles is achieved through initial compaction and a thin, secondary carbon coating applied via low-temperature CVD, which coats the granules externally but leaves the core binder intact. At the end of the battery's life, immersion of the anode in a dilute acid (e.g., 0.1M acetic acid) or base (e.g., 0.1M NaOH) causes the PLGA/chitosan binder to dissolve, allowing the granules to de-aggregate into individual nanocomposites, thereby facilitating the separation and recycling of silicon and carbon components with reduced energy input.

graph TD
    A[Graphitic Carbon Black NPs] -- Annealing --> B(Carbon Dendritic Particle)
    B -- PECVD (Si) --> C{Nanocomposite: C Dendrite + Discrete Si NPs}
    C -- Wet Granulation (pH-Sensitive Binder) --> D[Granule with Sacrificial Binder]
    D -- Low-Temp C-CVD (External Coating) --> E(Anode Granules with Electrical Comm.)
    E -- End-of-Life Disposal --> F(pH-Controlled De-Aggregation)
    F --> G(Separated Si/C for Recycling)

Combination Prior Art Scenarios

These scenarios combine elements of US Patent 11515528 (or its derivatives as disclosed above) with existing open-source standards, further broadening the defensive publication.

Anode Synthesis Process Control (Derivative 4.4) + OPC UA:
A manufacturing method for US11515528-type anodes (specifically Derivative 4.4, Digital Twin Guided Manufacturing) where all process parameters (e.g., CVD temperatures, gas flow rates, granulation speeds, binder injection rates) are communicated and controlled using the OPC Unified Architecture (OPC UA) open-source standard. Sensor data from the IoT devices (temperature, pressure, gas concentration, imaging for morphology) is standardized and exchanged via OPC UA servers and clients, enabling interoperability between different manufacturing equipment and the central AI-driven digital twin for real-time optimization. This scenario makes the integration of standard industrial communication protocols with advanced anode manufacturing obvious.
Real-time Anode Performance Monitoring (Derivative 1.4) + Open Charge Point Protocol (OCPP):
A lithium-ion battery incorporating US11515528-type anodes (specifically Derivative 1.4, AI-Optimized Smart Anode) where the embedded IoT sensors (temperature, strain) transmit real-time state-of-health and state-of-charge data. This data, along with optimized charge/discharge profiles generated by the AI-driven BMS, is communicated to external charging infrastructure using the Open Charge Point Protocol (OCPP). The OCPP messages are extended to include granular anode performance metrics, allowing charging stations to dynamically adjust charging behavior (e.g., current, voltage, ramp rates) based on the individual battery's anode health, maximizing battery lifespan and safety. This makes the real-time, data-driven optimization of charging protocols for advanced anodes, leveraging an open-source standard for EV charging, obvious.
Material Provenance and Recycling (Derivative 1.4 and 4.5) + Hyperledger Fabric:
A comprehensive system for managing the entire lifecycle of US11515528-type anode materials (incorporating both Derivative 1.4, Blockchain for provenance, and Derivative 4.5, Reversible De-Aggregation for recycling). All stages of raw material sourcing (e.g., silicon purity, carbon black origin), manufacturing process parameters, quality control checks, battery integration, and end-of-life collection and de-aggregation events are immutably recorded on a distributed ledger technology (DLT) platform, specifically using Hyperledger Fabric. Smart contracts on Hyperledger Fabric automate compliance checks, track material flows for recycling incentives, and trigger de-aggregation protocols. This establishes the obviousness of using robust, open-source blockchain frameworks for transparent and verifiable lifecycle management of advanced battery components, particularly those designed for circular economy principles.