Derivative works

Defensive Disclosure and Prior Art Publication

Title: Derivative Systems and Methods for Controlled, Real-Time Volumetric Fluid Separation
Publication Date: 2026-05-13
Keywords: Apheresis, plasma collection, fluid separation, real-time control, hematocrit, anticoagulant, process control, AI, IoT, microfluidics, cross-domain application.

Abstract: This document discloses novel methods, systems, and applications derived from the core principles of U.S. Patent 10,758,652. The disclosures herein are intended to enter the public domain to serve as prior art against future patent claims on incremental or obvious improvements. The disclosed concepts include alternative component architectures, operation in extreme environments, applications in non-medical fields such as aerospace and agriculture, integration with emerging technologies like artificial intelligence and blockchain, and the implementation of fail-safe and limited-functionality operational modes.

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Part 1: Derivative Methods for Pure Component Collection (Based on Claim 1)

Axis 1: Material & Component Substitution

Derivative 1.1: Method Using Acoustic Microfluidic Separation

• Enabling Description: This method replaces centrifugal separation with an acousto-microfluidic separation chip. Whole blood mixed with anticoagulant is pumped through a microfluidic channel containing piezoelectric transducers. The transducers generate a standing surface acoustic wave (SSAW) across the channel. As blood flows through, the acoustic radiation force directs larger, denser particles (red blood cells) to the pressure nodes of the standing wave, while smaller particles (platelets) and the plasma fluid are less affected. This results in the separation of blood into distinct streams. The controller calculates the volume of pure plasma by monitoring the flow rate in the plasma outlet channel and subtracting the calculated anticoagulant volume based on the known inlet flow rates of blood and anticoagulant, and the donor's hematocrit. The hematocrit is determined pre-procedure using a miniaturized resistive pulse sensor integrated into the fluidic chip.
• Mermaid Diagram:

  flowchart TD
      A[Donor] -->|Whole Blood| B(Micro-Pump);
      C[Anticoagulant] --> D(AC Pump);
      B & D --> E{Mixing Junction};
      E --> F[Acousto-Microfluidic Chip];
      subgraph F
          direction LR
          F1(Inlet) --> F2{SSAW Transducers};
          F2 --> F3(RBC Outlet Stream);
          F2 --> F4(Plasma Outlet Stream);
      end
      F4 --> G[Plasma Collection Container];
      F3 --> H{RBC Reservoir for Return};
      I[Controller] -->|Control SSAW Freq.| F2;
      G -->|Flow Rate| I;
      D -->|AC Flow Rate| I;
      I -->|Stop Pumps| B & D;
      I -- Calculates --> J(Pure Plasma Volume);
  

Derivative 1.2: Method Using Non-Contact Raman Spectroscopy for Hematocrit and Anticoagulant Measurement

• Enabling Description: This method replaces the initial hematocrit determination and the reliance on pump-rotation counting with a non-contact Raman spectroscopy sensor. The sensor is positioned on the exterior of the transparent draw line tubing. It directs a laser (e.g., 785 nm) into the flowing anticoagulated blood. The backscattered light is analyzed for its Raman spectrum. The controller uses a pre-calibrated chemometric model to simultaneously quantify (a) the hematocrit, based on the characteristic peaks of hemoglobin, and (b) the concentration of anticoagulant (e.g., citrate), based on its unique spectral signature. This provides a direct, continuous measurement of the anticoagulant-to-plasma ratio. The controller integrates this ratio against the total volume collected (measured by a weight sensor) to calculate the real-time volume of pure plasma, stopping the collection when the target is met.
• Mermaid Diagram:

  sequenceDiagram
      participant Donor
      participant DrawLine
      participant RamanSensor
      participant Controller
      participant CollectionContainer
  
      Donor->>DrawLine: Whole Blood
      loop Real-time Monitoring
          RamanSensor->>DrawLine: Emits Laser
          DrawLine-->>RamanSensor: Backscattered Light
          RamanSensor->>Controller: Transmits Spectrum Data
          Controller->>Controller: Analyzes Spectrum (Hct, AC %%)
          Controller->>CollectionContainer: Reads Total Weight
          Controller->>Controller: Calculates Pure Plasma Volume
          alt Pure Plasma Volume >= Target
              Controller->>DrawLine: Stop Pumps
              break
          end
      end
  

Axis 2: Operational Parameter Expansion

Derivative 2.1: Method for Plasmapheresis in Microgravity

• Enabling Description: This method adapts the process for a microgravity environment where gravitational sedimentation is absent. It uses an axial-flow centrifugal device where fluid dynamics, not density-based settling, dominate separation. The controller's calculation of pure plasma remains critical. However, all volume and weight measurements are replaced with non-gravimetric methods. Flow rates are measured by ultrasonic transit-time flow meters. The "weight" of the collected plasma and anticoagulant sources is determined by a change-in-volume measurement using capacitance-based level sensors within rigid containers. The target pure plasma volume is adjusted based on AI-predicted fluid shifts in the astronaut's body, using inputs from bioimpedance sensors. The system is designed for minimal power draw and operates in a fully closed loop to prevent fluid escape.
• Mermaid Diagram:

  graph TD
      subgraph Closed Loop System
          A(Astronaut) <--> B(Venous Access);
          B --> C{Axial-Flow Centrifuge};
          D(Anticoagulant) --> E(AC Pump);
          E --> B;
          C -- Plasma+AC --> F[Plasma Container];
          C -- RBCs --> G[RBC Container];
          G --> B;
      end
      subgraph Controller Unit
          H(Controller) -- Controls --> C & E;
          I(Ultrasonic Flow Meter) -- Measures Flow --> H;
          J(Capacitance Sensor) -- Measures Volume in F --> H;
          K(Bioimpedance Sensor on A) -- Measures Fluid Shift --> H;
          H -- Calculates --> L(Pure Plasma Volume);
      end
  

Axis 3: Cross-Domain Application

Derivative 3.1 (Aerospace): Real-Time Hydrazine Fuel Purification

• Enabling Description: A method for purifying monopropellant hydrazine fuel during long-duration space missions. The system draws fuel from a main tank, introduces a proprietary scavenging agent to bind with contaminants (e.g., aniline, water), and passes the mixture through a membrane separator. A controller calculates the volume of pure hydrazine collected in a purified tank. It does this by measuring the total volume transferred and subtracting the volume of the scavenging agent and the calculated volume of separated contaminants. Contaminant volume is calculated based on readings from an inline optical density sensor, which correlates turbidity to contaminant concentration. The process stops when a target volume of 99.99% pure hydrazine is ready for the reaction control thrusters.
• Mermaid Diagram:

  flowchart TD
      A[Main Fuel Tank] --> B{Pump};
      C[Scavenging Agent] --> D{Dosing Pump};
      B & D --> E[Membrane Separator];
      E -- Purified Fuel --> F(Purified Tank);
      E -- Contaminants+Agent --> G(Waste Tank);
      H[Controller] -->|Control Pumps| B & D;
      I[Optical Density Sensor] -- Contaminant % --> H;
      J[Volume Sensor on F] -- Total Volume --> H;
      H -- Calculates --> K[Pure Hydrazine Volume];
  

Derivative 3.2 (AgTech): Phycocyanin Extraction from Spirulina Slurry

• Enabling Description: A method for extracting high-value phycocyanin protein from a raw spirulina algae slurry. The slurry is pumped from a bioreactor, and a flocculant is introduced to clump cellular debris. The mixture is then processed in a tangential flow filtration (TFF) system. The controller calculates the volume of pure phycocyanin concentrate collected. It uses an inline spectrophotometer (measuring absorbance at ~620nm) to determine the concentration of phycocyanin in the permeate (the collected fluid). The volume of flocculant is known from the pump rate. The controller integrates the concentration over the total collected volume to calculate the mass, and thus the equivalent volume of pure concentrate, stopping the process when a target yield is achieved.
• Mermaid Diagram:

  graph TD
      A[Spirulina Bioreactor] --> B[Slurry Pump];
      C[Flocculant] --> D[Dosing Pump];
      B & D --> E[Tangential Flow Filtration Skid];
      E -- Permeate --> F(Phycocyanin Concentrate Tank);
      E -- Retentate (Waste) --> G(Debris Tank);
      H[Controller] -- Manages --> B & D & E;
      I[Spectrophotometer] -- Measures Purity of Permeate --> H;
      J[Weight Sensor on F] -- Measures Total Volume --> H;
      H -- Calculates --> K[Pure Concentrate Yield];
  

Axis 4: Integration with Emerging Tech

Derivative 4.1 (AI/ML): Predictive Method for Dynamic Anticoagulant Dosing

• Enabling Description: An AI-enhanced method where the controller uses a machine learning model to dynamically adjust the anticoagulant (AC) to whole blood (WB) ratio in real-time. Before the procedure, the controller ingests donor data (weight, hematocrit, age, donation history). During the procedure, it monitors real-time data from pressure sensors in the draw line and the separation device. The ML model, trained on thousands of previous donations, predicts the likelihood of a flow-impeding clot or high shear stress based on these inputs. If the risk increases, the controller preemptively increases the AC:WB ratio slightly. Conversely, if the risk is low, it decreases the ratio, minimizing citrate load on the donor. The calculation of pure plasma continuously adapts to this variable AC ratio, ensuring the final target volume remains accurate.
• Mermaid Diagram:

  stateDiagram-v2
      state "Data Ingestion" as Ingest
      state "Procedure Start" as Start
      state "Real-time Monitoring & Prediction" as Monitor
      state "Dynamic Adjustment" as Adjust
      state "Calculation" as Calc
      state "End Procedure" as End
  
      [*] --> Ingest: Donor Data (Hct, Wt, Hist)
      Ingest --> Start: Initialize AC:WB Ratio
      Start --> Monitor
      Monitor --> Adjust: ML Model predicts high risk
      Adjust --> Monitor: Increase AC:WB ratio
      Monitor --> Monitor: ML Model predicts low risk
      Monitor --> Calc: Stream Sensor Data
      Calc --> Monitor: Update Pure Plasma Vol
      Monitor: If Pure Plasma Vol >= Target
      Monitor --> End
      [*] --> End: Manual Stop / Error
  

Axis 5: The "Inverse" or Failure Mode

Derivative 5.1: Fail-Safe Blood Component Return Method

• Enabling Description: A method designed for safe failure resolution. Upon detection of a non-recoverable error (e.g., centrifuge imbalance, disposable kit leak detected by pressure decay), the controller immediately enters a "Safe Return" mode. It stops the draw and AC pumps and calculates three key volumes: (1) Total RBCs in the separation device, (2) Total extracorporeal plasma, and (3) Total extracorporeal anticoagulant. Its primary objective is to return the viable RBCs. It uses the remaining anticoagulant in the source bag to prime the return line and gently re-infuse the RBCs. The controller logs the exact volume of pure plasma lost (extracorporeal plasma minus extracorporeal anticoagulant) and flags the donor's record with a temporary deferral period calculated based on this loss, ensuring regulatory compliance.
• Mermaid Diagram:

  flowchart TD
      A{Procedure Active} --> B{Error Detected!};
      B --> C[Enter Safe-Return Mode];
      C --> D[Stop Draw Pump & Centrifuge];
      C --> E{Calculate Extracorporeal Vols};
      subgraph E
          E1(RBCs in Device)
          E2(Plasma in Device/Line)
          E3(AC in Device/Line)
      end
      C --> F[Log Pure Plasma Lost];
      C --> G{Initiate RBC Return};
      H(Anticoagulant Source) --> |Prime Line| I(Return Line);
      J(RBCs from Device) --> I;
      I --> K(Donor);
      G --> L[Log RBC Volume Returned];
      L --> M[Set Temporary Donor Deferral];
  

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Part 2: Derivative Systems for Pure Component Collection (Based on Claim 11)

Axis 1: Material & Component Substitution

Derivative 6.1: System with Piezoelectric Valveless Pumps

• Enabling Description: This system replaces traditional peristaltic pumps with piezoelectric micropumps for both blood draw and anticoagulant delivery. These pumps have no moving mechanical parts, operating via the controlled oscillation of a piezoelectric diaphragm, which significantly reduces hemolysis (red blood cell damage). They also offer extremely precise, pulsation-free flow control. The controller is configured with the precise displacement-per-voltage-cycle characteristics of the piezo pumps. It calculates the total volume of anticoagulant delivered by integrating the applied voltage waveform over time. This highly accurate volume data is used in the algorithm to calculate the pure plasma volume collected in the plasma container.
• Mermaid Diagram:

  classDiagram
      class Controller {
          +calculatePurePlasma()
          +controlPumpVoltage(pumpID, waveform)
          -targetVolume
          -donorHct
      }
      class PiezoelectricPump {
          +setVoltageWaveform(waveform)
          -displacementPerCycle
      }
      class WeightSensor {
          +readWeight()
      }
      Controller "1" -- "2" PiezoelectricPump : Controls
      Controller "1" -- "1" WeightSensor : Reads
      PiezoelectricPump : Blood Draw Pump
      PiezoelectricPump : Anticoagulant Pump
  

Axis 2: Operational Parameter Expansion

Derivative 7.1: Wearable System for Continuous Ambulatory Plasma Exchange

• Enabling Description: A miniaturized, wearable system for therapeutic plasma exchange (TPE) in ambulatory patients. The system is housed in a lightweight, body-worn pack and uses a dual-lumen subcutaneous catheter. Separation is achieved via a disposable hollow-fiber plasma filter cartridge instead of a centrifuge. The system operates at extremely low flow rates (5-10 mL/min) over many hours. The controller's function is critical: it calculates the cumulative net pure plasma removed over the entire therapeutic period, accounting for both the added anticoagulant and the periodic infusion of a replacement fluid (e.g., albumin). The controller uses data from miniature optical and pressure sensors to manage the filtration gradient and prevent filter clogging, adjusting pump speeds to maintain a target pure plasma removal rate.
• Mermaid Diagram:

  graph TD
      subgraph Wearable Pack
          A(Dual-Lumen Catheter) -- Blood --> B(Blood Pump);
          C(Anticoagulant) --> D(AC Pump);
          D --> B;
          B --> E(Hollow-Fiber Filter);
          E -- Plasma+AC --> F(Waste Bag);
          E -- RBCs --> G(Return Pump);
          H(Replacement Fluid) --> I(RF Pump);
          I & G --> A;
      end
      subgraph Controller
          J(Controller) -- Manages --> B,D,G,I;
          K(Optical Sensor on E) -- Filter Status --> J;
          L(Pressure Sensor) -- Transmembrane Pressure --> J;
          M(Weight Sensor on F) -- Waste Vol --> J;
          J -- Calculates --> N(Net Pure Plasma Removed);
      end
  

Axis 3: Cross-Domain Application

Derivative 8.1 (Consumer Electronics): Smart Reverse Osmosis Water Purifier

• Enabling Description: A home reverse osmosis (RO) system with a smart controller. The controller is configured to calculate the volume of pure, filtered water produced and stop the system when a user-defined target (e.g., 1 liter) is met. It measures the total volume of water flowing into the RO membrane using a simple impeller flow meter. A conductivity sensor measures the total dissolved solids (TDS) of both the inlet tap water and the outlet brine (waste) water. The controller uses this TDS differential to calculate the rejection rate and thus the volume of water being sent to waste. The pure water volume is calculated as: (Total Inlet Volume) - (Calculated Waste Volume). This allows the system to optimize for water efficiency and accurately track filter life based on pure water produced, not just run time.
• Mermaid Diagram:

  sequenceDiagram
      participant User
      participant Controller
      participant RO_System
      participant PureWaterTank
  
      User->>Controller: Set Target Volume (1.0 L)
      Controller->>RO_System: Start Pumps
      loop Purification Cycle
          RO_System->>Controller: Send Inlet Flow & TDS data
          RO_System->>Controller: Send Brine TDS data
          Controller->>Controller: Calculate Waste Volume
          Controller->>Controller: Calculate Pure Water Volume
          Controller->>PureWaterTank: Update Current Volume
          alt Pure Water >= Target
              Controller->>RO_System: Stop Pumps
              break
          end
      end
      Controller->>User: Notify: "Purification Complete"
  

Axis 4: Integration with Emerging Tech

Derivative 9.1 (IoT/Blockchain): Verifiable Cold Chain System for Plasma

• Enabling Description: A system where the apheresis controller is the first node in a blockchain-secured cold chain. When the target pure plasma volume is reached, the controller generates a "genesis block" for that specific plasma unit. This block contains the donor's anonymized ID, the final pure plasma volume, hematocrit, anticoagulant lot number, and a timestamp. The plasma collection container is sealed with an IoT tag containing a temperature logger and a GPS chip. As the plasma unit is transported, the IoT tag periodically writes new blocks to the chain with its temperature and location data. Any temperature excursion outside the safe range is immutably recorded. This creates a verifiable, end-to-end audit trail from collection to fractionation, ensuring supply chain integrity.
• Mermaid Diagram:

  flowchart TD
      A[Controller Calculates Pure Plasma Vol];
      A -- Vol >= Target --> B{Finalize Collection};
      B --> C[Generate Genesis Block];
      subgraph C
          D(Donor ID_anon)
          E(Pure Plasma Vol)
          F(Timestamp)
      end
      C --> G[Write to Blockchain];
      H(Plasma Bag) -- Sealed with --> I(IoT Tag);
      I -- Contains Hash of Block C --> G;
      I -- Temp/GPS Data --> J{Transport Phase};
      J -- Periodically --> K[Create & Add New Blocks];
      K --> G;
      G --> L[Immutable Ledger];
  

Axis 5: The "Inverse" or Failure Mode

Derivative 10.1: System with a "Safe Yield" Low-Power Mode

• Enabling Description: A system controller configured with a "Safe Yield" mode for operating in low-power or emergency situations (e.g., on battery backup). When activated, the controller reduces the centrifuge speed by 30% and the blood draw pump speed by 40% to conserve energy. This reduces separation efficiency. The controller's algorithm switches to a different model that accounts for this lower efficiency, resulting in a higher likelihood of red blood cell contamination in the plasma line. To compensate, an optical sensor on the plasma line is used to detect hemolysis or RBC spillover. If detected, the plasma flow is temporarily diverted to an internal waste pouch. The controller calculates the pure plasma volume collected in the primary container, subtracting both the anticoagulant volume and the diverted waste volume. The overall target volume of pure plasma is automatically reduced by 20% to ensure the procedure completes before battery depletion.
• Mermaid Diagram:

  stateDiagram-v2
      state "Normal Operation" as Normal
      state "Low-Power Mode" as LowPower
      state "Diversion Active" as Divert
  
      [*] --> Normal
      Normal --> LowPower: Power Loss Detected
      LowPower --> Normal: Main Power Restored
      LowPower: Reduce RPM & Flow Rate
      LowPower: Reduce Target Volume
      LowPower --> Divert: RBC Spillover Detected
      Divert --> LowPower: Spillover Clears
      Divert: Route Plasma to Waste Pouch
      Divert: Subtract Waste from Total
      LowPower --> [*]: Procedure Complete
      Normal --> [*]: Procedure Complete
  

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Part 3: Combination Prior Art Scenarios

Scenario 1: Integration with the HL7 FHIR Standard

• Disclosure: The system described in Claim 11 is combined with the Health Level Seven International (HL7) Fast Healthcare Interoperability Resources (FHIR) standard. The apheresis system's controller acts as a FHIR client. Upon completion of a donation, it creates a "DiagnosticReport" FHIR resource. This resource includes the donor's identifier, the final calculated pure plasma volume, the average anticoagulant ratio used, and the initial hematocrit value. This DiagnosticReport is then securely transmitted via a RESTful API to the Blood Bank's FHIR-compliant server, enabling seamless integration with the electronic health record (EHR) and laboratory information systems (LIS) without proprietary data formats.

Scenario 2: Combination with the OpenAPS (Open Artificial Pancreas System) Framework

• Disclosure: The method of Claim 1 is combined with the control logic and communication protocols of the open-source OpenAPS project. The apheresis controller runs a modified OpenAPS algorithm. Instead of blood glucose, the primary input is real-time hematocrit (from an inline sensor). Instead of insulin, the controller modulates the anticoagulant flow rate. The system's objective is to maintain a target "anticoagulation index" in the extracorporeal circuit, minimizing clotting risk while also minimizing the total citrate delivered to the donor. The pure plasma calculation is a secondary process that determines the procedure endpoint. This combination applies a proven, open-source closed-loop control algorithm to the field of apheresis.

Scenario 3: Combination with the RISC-V Open Instruction Set Architecture

• Disclosure: The controller of the system in Claim 11 is built not on a proprietary microprocessor, but on a processor implementing the open-source RISC-V instruction set architecture (ISA). The specific software and firmware for calculating pure plasma volume, controlling the pumps, and monitoring sensors is compiled for a RISC-V core (e.g., a 32-bit RV32IMC core). This disclosure places the implementation of this specific apheresis control logic on an open, auditable, and royalty-free hardware standard into the public domain, preventing any single company from claiming a patent on the use of a specific type of processor for this task. The firmware itself could be made open-source to allow for third-party verification of the pure plasma calculation algorithm for regulatory purposes.