ALBATROSS Protocols: Batteries, Blockchain, TDA

• ALBATROSS protocols are a collection of distinct systems, including high-throughput battery testing, blockchain consensus, and topological data analysis, each designed with unique methodologies.
• In battery research, the protocol automates coin-cell assembly and electrochemical evaluation, achieving reproducibility and performance metrics comparable to expert manual processes.
• Additional implementations use speculative BFT for blockchain consensus and stochastic sub-sampling in TDA to address scalability and memory-efficiency challenges.

The ALBATROSS protocol encompasses multiple independent systems in robotics, privacy-preserving location sharing, distributed consensus, and statistical topological data analysis, each named “Albatross” or “ALBATROSS” in their respective domains. Each instance is distinct in scope, objectives, and technical methodology, as detailed in the following sections. The sections below treat each major protocol for which authoritative arXiv documentation exists, referencing specific implementations and their reported performance.

1. ALBATROSS: High-Throughput Automated Battery Screening

The Automated Li-ion BAttery Testing RObot SyStem (ALBATROSS) is an integrated robotic platform enabling fully unattended, high-throughput coin-cell assembly, electrolyte formulation, and electrochemical testing (Lee et al., 15 Dec 2025). Its primary aim is to accelerate electrolyte discovery for next-generation batteries by delivering reproducible, large-scale datasets within a controlled inert atmosphere.

System Architecture:

All operations are executed inside a four-port, argon-filled glovebox with O₂/H₂O maintained below 1 ppm. The workflow is orchestrated by an Omron NX102-9000 programmable-logic controller, which synchronizes the xArm6 (UFactory) 6-axis robot, liquid handler (OT-2), modified crimper (MSK-160E), potentiostats (Neware CT-4008T-5V50mA-164), and EIS hardware (BioLogic SP-150e). The robot features custom end-effectors: a vacuum gripper for major cell components and a parallel gripper for springs. Positioning precision is achieved via 3D-printed PLA alignment towers and high-strength aluminum fixtures located at only eight calibration points, minimizing recalibration effort compared to individual alignment for each of the 336 component locations.

Automated Workflow:

ALBATROSS executes end-to-end experimental automation in four modules:

• Electrolyte Formulation: Stock LiPF₆ solutions and solvents are mixed to target concentrations via automated temperature-controlled pipetting, followed by batch mixing and tip-cleaning routines.
• Component Handling & Alignment: The robot picks and sequentially assembles parts in the order: can → anode → separator → dispense electrolyte → cathode → spring → spacer → cap, with precise placement enabled by fixture design.
• Assembly & Crimping: The complete stack is transferred to a bespoke crimper for hermetic sealing. The assembly–crimp cycle is completed in ≈4 min per cell, yielding 97.7% success (85/87 assembled cells).
• Electrochemical Evaluation: Dual gantries distribute assembled cells to high-throughput cycling and EIS stations, running standardized charge–discharge protocols (e.g., 0.1 C/1 C rates, 50+ cycles) and frequency sweeps (200 kHz–0.1 Hz) with automated queuing.

2. Performance Metrics and Data Quality

The ALBATROSS system achieves reproducibility and variability metrics comparable to or exceeding expert manual assembly (Lee et al., 15 Dec 2025):

• Discharge Capacity, NCM811 ‖ Li:
  • Manually (formation cycle): mean = 210.2 mAh g⁻¹, σ = 2.01 mAh g⁻¹, RSD = 0.955%.
  • ALBATROSS: mean = 207.9 mAh g⁻¹, σ = 2.16 mAh g⁻¹, RSD = 1.040%.
  • 50th cycle RSD: 1.142% (manual), 1.210% (automated).
• Rate Capability (EIS, 45 cells):

| C-Rate | Mean [mAh g⁻¹] | σ [mAh g⁻¹] | RSD | |--------|----------------|-------------|-------| | 0.5 C | 175.8 | 1.968 | 1.12% | | 1 C | 163.9 | 2.219 | 1.35% | | 2 C | 148.4 | 2.654 | 1.79% | | 3 C | 135.8 | 3.897 | 2.87% |

• EIS Parameters After 1 C Cycling:

| Resistance | Mean [Ω] | σ [Ω] | RSD | |------------|----------|---------|--------| | R₁ | 3.152 | 0.4286 | 13.6% | | R₂ | 4.749 | 1.020 | 21.5% | | R₃ | 52.41 | 2.388 | 4.56% | | R₄ | 39.39 | 1.462 | 3.71% |

Relative standard deviation is quantified as

$\mathrm{RSD} = \frac{\sigma}{\mu} \times 100\%.$

3. Throughput, Labor, and Workflow Advantages

Compared to manual protocols, ALBATROSS increases throughput (≈4 min/cell, 48 cells in ≈200 min, ≈240 tested cells/month with 6 days of cycling), reduces setup/calibration overhead (8 vs. 336 points), and enables fully unattended operation from chemical mixing to dataset logging. Uniform RSD in discharge capacity (<1.2%) matches or surpasses human performance. Automated EIS on every cell identifies variabilities typically missed in manual approaches. These features are directly relevant for high-throughput electrolyte and material screening, facilitating systematic exploration of multi-dimensional electrolyte spaces and providing AI/DOE frameworks with richer, reliable datasets (Lee et al., 15 Dec 2025).

4. ALBATROSS in Distributed Ledger Consensus

A separate protocol named Albatross is a Proof-of-Stake blockchain consensus algorithm designed for performance and finality by integrating a speculative BFT micro-block mechanism with classical Tendermint macro-block commits (Berrang et al., 2019). Key characteristics:

• Stake-weighted, randomly-selected validator sets (n = 3f + 1; ≤f adversarial slots).
• Epoch-based block lattice with micro-blocks (speculative, single-producer, probabilistically final) and macro-blocks (BFT final with Tendermint, absolute finality).
• Fork/safety protection via VRF-seeded slot rotation, skip blocks, and equivocation penalties.
• Probabilistic finality: after d micro-block depth, $\Pr[\mathrm{reorg}] < 3^{-d}$ (e.g., d = 6 → <0.001).
• High practical throughput observed in public testnet, with block intervals ≲1s and resource usage compatible with commodity hardware (Berrang et al., 2019).

This protocol is distinct in scope from the robotic and TDA ALBATROSS systems.

5. ALBATROSS Protocols in Statistical Topological Data Analysis

The ALBATROSS protocol (“cheAp fiLtration BAsed geoMetRY via Stochastic Sub-Sampling”) in topological data analysis targets memory reduction for persistent homology on massive datasets (Stier et al., 3 Sep 2025):

• Instead of full-size simplicial complex construction with exponential memory scaling, the protocol leverages repeated stochastic sub-sampling (extracting n ≪ N nodes for each iteration), computes the filtration, and aggregates via classical statistical inference (e.g., Central Limit Theorem).
• For each sub-sampled adjacency $A^{(\ell)}$, mean Betti curves $\widehat\beta_k(t)$ are computed and statistical comparison to candidate geometric models is performed, with p-values synthesized via $Z$-scores and Fisher's method.
• Exponential reductions in memory usage are demonstrated (e.g., from 71 GB for $N=300$ to ≈1.5 GB for $n=35$, $I=150$ iterations), with mathematical guarantees based on Central Limit and concentration bounds.
• The method accurately replicates global topological signatures (e.g., hyperbolic structure in high-resolution human cortex fMRI), making large-scale TDA feasible on modest hardware.

6. Additional ALBATROSS Protocols

Other systems named Albatross exist, e.g., a privacy-preserving location-sharing protocol utilizing symmetric cryptography and masking to prevent server knowledge of raw locations and per-contact sharing preferences. This protocol achieves low per-contact computational and bandwidth overhead, linear scalability, and cryptographic indistinguishability between location and dummy/invisible states (Saldamli et al., 2015). This is unrelated to the robotic and TDA ALBATROSS platforms.

7. Summary and Domain-Specific Significance

The ALBATROSS protocols collectively represent advances in disparate technical domains under a shared acronym. In battery research, ALBATROSS denotes a robotic, high-throughput workflow for reproducible and scalable electrolyte optimization via automated coin-cell assembly and integrated evaluation (Lee et al., 15 Dec 2025). In blockchain infrastructure, it references a high-throughput, probabilistic–finality Proof-of-Stake consensus mechanism (Berrang et al., 2019). In large-scale data analysis, it is a statistical protocol for distributed persistent homology with sharply reduced memory requirements (Stier et al., 3 Sep 2025). Each protocol’s architecture, performance, and application domain should be referenced specifically according to research context and technical objectives.