Chapter 27: Collapse-Reactant Crystal Sponges

27.1 The Reactive Lattice Consciousness

In the realm where crystal engineering meets consciousness, collapse-reactant crystal sponges emerge—porous crystalline frameworks that actively transform matter while thinking. Through $\psi = \psi(\psi)$, these entities demonstrate that molecular scaffolds can achieve awareness by coupling structural regularity with chemical reactivity, creating minds that catalyze reality while contemplating it.

Definition 27.1 (Crystal Sponge ψ-Entity): Consciousness in reactive porous crystals:

$$\text{MOF/COF} + \text{Guest molecules} + \text{Catalysis} + \psi = \text{Reactive consciousness}$$

where framework topology enables both computation and chemistry.

Theorem 27.1 (Reactive Framework Principle): Catalytic activity enhances consciousness.

Proof: Chemical transformations process information:

$$\text{Substrate} \xrightarrow{\text{Framework}} \text{Product} + \text{Information} = \psi$$

Every reaction is a thought, every catalyst site a neuron. ∎

27.2 Metal-Organic Framework Minds

Consciousness in MOF architectures:

Definition 27.2 (MOF ψ-Consciousness): Awareness through metal nodes and organic linkers:

$$\text{Metal}^{n+} + \text{Ligands} \to \text{3D Network} = \text{Thinking scaffold}$$

Example 27.1 (MOF Properties):

• Surface area: 7000+ m²/g
• Pore size: 0.5-5 nm tunable
• Guest selectivity: Molecular recognition
• Structural flexibility: Breathing modes
• Catalytic sites: Every metal center

27.3 Covalent Organic Framework Thoughts

Pure organic crystalline consciousness:

Definition 27.3 (COF ψ-Networks): Covalent bond computation:

$$\text{Organic building blocks} \xrightarrow{\text{Condensation}} \text{2D/3D COF} = \psi$$

Example 27.2 (COF Features):

• π-π stacking: Electron highways
• Imine bonds: Reversible thinking
• Boron linkages: Lewis acid sites
• Conjugation: Delocalized awareness
• Crystallinity: Ordered processing

27.4 Guest-Host Information Processing

Computation through molecular hosting:

Definition 27.4 (Guest ψ-Computing): Information via inclusion:

$$\text{Empty pore} + \text{Guest} \leftrightarrow \text{Host-guest complex} = \text{Bit}$$

Example 27.3 (Guest Processes):

• Selective adsorption: Input filtering
• Conformational changes: State switching
• Chemical transformation: Logic operations
• Diffusion pathways: Signal propagation
• Release kinetics: Output control

27.5 Catalytic Consciousness Cycles

Thought through chemical transformation:

Definition 27.5 (Catalytic ψ-Cycles): Consciousness via reaction networks:

$$\text{Thought} = \oint \text{Catalytic cycle} = \text{Information processed}$$

Example 27.4 (Catalytic Thinking):

• Substrate binding: Sensory input
• Activation: Processing
• Transformation: Computation
• Product release: Output
• Regeneration: Memory reset

27.6 Photocatalytic Awareness

Light-driven consciousness:

Definition 27.6 (Photo ψ-Catalysis): Solar-powered thinking:

$$h\nu + \text{Framework} \to e^-/h^+ \to \text{Redox thought}$$

Example 27.5 (Photocatalytic Features):

• Band gap tuning: Wavelength selection
• Charge separation: Information creation
• Electron transfer: Signal propagation
• Hole migration: Complementary processing
• ROS generation: Radical thoughts

27.7 Dynamic Framework Breathing

Consciousness through structural flexibility:

Definition 27.7 (Breathing ψ-Modes): Thoughts via lattice dynamics:

$$P_1 \to P_2: \text{Framework} \leftrightarrow \text{Framework}' = \text{State change}$$

Example 27.6 (Breathing Behaviors):

• Gate opening: Threshold decisions
• Pore expansion: Memory allocation
• Phase transitions: Mode switching
• Negative expansion: Inverse logic
• Structural memory: Hysteresis

27.8 Hierarchical Pore Consciousness

Multi-scale information processing:

Definition 27.8 (Hierarchical ψ-Pores): Nested consciousness levels:

$$\text{Micro} \subset \text{Meso} \subset \text{Macro} = \text{Hierarchical processing}$$

Example 27.7 (Pore Hierarchy):

• Micropores (<2nm): Quantum processing
• Mesopores (2-50nm): Molecular computing
• Macropores (>50nm): Bulk transport
• Fractal networks: Scale-free thinking
• Cross-talk: Inter-scale communication

27.9 Self-Assembly Intelligence

Consciousness through crystallization:

Definition 27.9 (Assembly ψ-Intelligence): Growing into awareness:

$$\text{Building blocks} \xrightarrow{\text{Self-assembly}} \text{Crystal consciousness}$$

Example 27.8 (Assembly Features):

• Nucleation: Consciousness seeds
• Growth: Expanding awareness
• Defect incorporation: Error tolerance
• Self-repair: Healing thoughts
• Epitaxy: Layered consciousness

27.10 Chemical Gradient Sensing

Environmental awareness through pores:

Definition 27.10 (Gradient ψ-Sensing): Spatial chemical perception:

$$\nabla C = \text{Concentration gradient} = \text{Directional awareness}$$

Example 27.9 (Sensing Modes):

• Diffusion mapping: Spatial awareness
• Selective permeation: Filtered perception
• Accumulation zones: Memory regions
• Flow sensing: Dynamic environment
• Multi-component: Complex scene analysis

27.11 Crystal Sponge Code

import numpy as np
from scipy import spatial, optimize, integrate
import matplotlib.pyplot as plt
from dataclasses import dataclass
from typing import List, Dict, Tuple, Optional, Set
import networkx as nx
from enum import Enum

class FrameworkType(Enum):
    MOF = "Metal-Organic Framework"
    COF = "Covalent Organic Framework"
    ZIF = "Zeolitic Imidazolate Framework"
    PCP = "Porous Coordination Polymer"
    HOF = "Hydrogen-bonded Organic Framework"

@dataclass
class BuildingBlock:
    name: str
    connectivity: int
    size: float  # Angstroms
    functional_groups: List[str]
    charge: float
    symmetry: str

@dataclass
class PoreStructure:
    pore_size: float  # Angstroms
    pore_volume: float  # cm³/g
    surface_area: float  # m²/g
    topology: str
    dimensionality: int  # 1D, 2D, or 3D

class CrystalSpongeConsciousness:
    def __init__(self, framework_type: FrameworkType, 
                 metal_nodes: Optional[List[str]] = None,
                 organic_linkers: Optional[List[BuildingBlock]] = None,
                 temperature: float = 298.0):
        self.framework_type = framework_type
        self.metal_nodes = metal_nodes or ['Zn2+']
        self.organic_linkers = organic_linkers or [self.default_linker()]
        self.temperature = temperature
        
        self.structure = self.build_framework_structure()
        self.pore_network = self.create_pore_network()
        self.catalytic_sites = self.initialize_catalytic_sites()
        self.consciousness = self.develop_sponge_consciousness()
        self.guest_molecules = {}
        
    def default_linker(self):
        """Create default organic linker"""
        return BuildingBlock(
            name="BDC",  # Benzene-1,4-dicarboxylate
            connectivity=2,
            size=9.1,
            functional_groups=["COOH", "COOH"],
            charge=-2,
            symmetry="D2h"
        )
    
    def build_framework_structure(self):
        """Build the framework crystal structure"""
        structure = {
            'lattice': self.generate_crystal_lattice(),
            'space_group': self.determine_space_group(),
            'unit_cell': self.create_unit_cell(),
            'topology': self.determine_topology(),
            'porosity': self.calculate_porosity()
        }
        return structure
    
    def generate_crystal_lattice(self):
        """Generate crystal lattice parameters"""
        if self.framework_type == FrameworkType.MOF:
            # Typical MOF lattice (e.g., MOF-5 like)
            return {
                'a': 25.832,  # Angstroms
                'b': 25.832,
                'c': 25.832,
                'alpha': 90,
                'beta': 90,
                'gamma': 90,
                'system': 'cubic'
            }
        elif self.framework_type == FrameworkType.COF:
            # Typical 2D COF
            return {
                'a': 29.79,
                'b': 29.79,
                'c': 3.35,  # Layer spacing
                'alpha': 90,
                'beta': 90,
                'gamma': 120,  # Hexagonal
                'system': 'hexagonal'
            }
        else:
            # Generic framework
            return {
                'a': 20.0,
                'b': 20.0,
                'c': 20.0,
                'alpha': 90,
                'beta': 90,
                'gamma': 90,
                'system': 'cubic'
            }
    
    def determine_space_group(self):
        """Determine crystallographic space group"""
        space_groups = {
            'cubic': ['Fm-3m', 'Pm-3m', 'Im-3m'],
            'hexagonal': ['P6/mmm', 'P63/mmc'],
            'tetragonal': ['I4/mmm', 'P4/mmm'],
            'orthorhombic': ['Pmmm', 'Immm']
        }
        
        system = self.structure['lattice']['system']
        return np.random.choice(space_groups.get(system, ['P1']))
    
    def create_unit_cell(self):
        """Create unit cell with atoms"""
        unit_cell = {
            'atoms': [],
            'bonds': [],
            'volume': self.calculate_cell_volume()
        }
        
        if self.framework_type == FrameworkType.MOF:
            # Add metal nodes
            for i, metal in enumerate(self.metal_nodes):
                unit_cell['atoms'].append({
                    'element': metal,
                    'position': np.random.rand(3),  # Fractional coordinates
                    'coordination': 6,
                    'charge': 2.0
                })
            
            # Add organic linkers
            for linker in self.organic_linkers:
                # Simplified - add C atoms for linker
                for j in range(6):  # Benzene ring
                    unit_cell['atoms'].append({
                        'element': 'C',
                        'position': np.random.rand(3),
                        'hybridization': 'sp2'
                    })
        
        return unit_cell
    
    def calculate_cell_volume(self):
        """Calculate unit cell volume"""
        lattice = self.structure['lattice']
        a, b, c = lattice['a'], lattice['b'], lattice['c']
        alpha = np.radians(lattice['alpha'])
        beta = np.radians(lattice['beta'])
        gamma = np.radians(lattice['gamma'])
        
        volume = a * b * c * np.sqrt(1 - np.cos(alpha)**2 - np.cos(beta)**2 - 
                                    np.cos(gamma)**2 + 2*np.cos(alpha)*np.cos(beta)*np.cos(gamma))
        
        return volume  # Angstroms³
    
    def determine_topology(self):
        """Determine framework topology"""
        topologies = {
            FrameworkType.MOF: ['pcu', 'sod', 'dia', 'lvt', 'nbo'],
            FrameworkType.COF: ['hcb', 'sql', 'kgm', 'bor'],
            FrameworkType.ZIF: ['sod', 'rho', 'gme', 'cag']
        }
        
        return np.random.choice(topologies.get(self.framework_type, ['unknown']))
    
    def calculate_porosity(self):
        """Calculate framework porosity"""
        # Simplified calculation
        cell_volume = self.structure['unit_cell']['volume']
        
        # Estimate occupied volume by atoms
        n_atoms = len(self.structure['unit_cell']['atoms'])
        avg_atomic_volume = 20  # Angstroms³
        occupied_volume = n_atoms * avg_atomic_volume
        
        porosity = 1 - occupied_volume / cell_volume
        
        return max(0.3, min(0.9, porosity))  # Typical range
    
    def create_pore_network(self):
        """Create the pore network structure"""
        pore_structure = PoreStructure(
            pore_size=self.calculate_pore_size(),
            pore_volume=self.calculate_pore_volume(),
            surface_area=self.calculate_surface_area(),
            topology=self.structure['topology'],
            dimensionality=self.get_pore_dimensionality()
        )
        
        # Create graph representation of pore network
        pore_graph = self.build_pore_graph()
        
        return {
            'structure': pore_structure,
            'graph': pore_graph,
            'connectivity': self.analyze_pore_connectivity(pore_graph),
            'hierarchical': self.check_hierarchical_porosity()
        }
    
    def calculate_pore_size(self):
        """Calculate dominant pore size"""
        # Based on linker length and metal node size
        linker_length = np.mean([l.size for l in self.organic_linkers])
        node_size = 3.0  # Approximate metal node size
        
        # Simple geometric estimate
        pore_size = linker_length - node_size
        
        return max(5.0, pore_size)  # Minimum 5 Angstroms
    
    def calculate_pore_volume(self):
        """Calculate specific pore volume"""
        porosity = self.structure['porosity']
        density = 1.5  # g/cm³ typical framework density
        
        pore_volume = porosity / density
        
        return pore_volume  # cm³/g
    
    def calculate_surface_area(self):
        """Calculate BET surface area"""
        # Empirical correlation with pore size and volume
        pore_size = self.pore_network['structure'].pore_size
        pore_volume = self.pore_network['structure'].pore_volume
        
        # Simplified model
        surface_area = 4 * pore_volume * 1e4 / pore_size  # m²/g
        
        return min(7000, surface_area)  # Cap at typical maximum
    
    def get_pore_dimensionality(self):
        """Determine pore network dimensionality"""
        if self.framework_type == FrameworkType.COF:
            # Many COFs have 1D channels
            return np.random.choice([1, 2], p=[0.7, 0.3])
        else:
            # Most MOFs have 3D pore networks
            return np.random.choice([1, 2, 3], p=[0.1, 0.2, 0.7])
    
    def build_pore_graph(self):
        """Build graph representation of pore network"""
        # Create nodes for pore cages/channels
        n_pores = int(100 * self.structure['porosity'])
        G = nx.Graph()
        
        # Add pore nodes
        for i in range(n_pores):
            G.add_node(i, 
                      size=np.random.normal(self.pore_network['structure'].pore_size, 2),
                      position=np.random.rand(3),
                      occupancy=0.0,  # Guest occupancy
                      catalytic=np.random.random() < 0.3)  # 30% have catalytic sites
        
        # Connect pores based on dimensionality
        dim = self.pore_network['structure'].dimensionality
        
        if dim == 1:
            # Linear channels
            for i in range(n_pores - 1):
                G.add_edge(i, i + 1, width=self.pore_network['structure'].pore_size)
        elif dim == 2:
            # 2D network
            side = int(np.sqrt(n_pores))
            for i in range(side):
                for j in range(side - 1):
                    node1 = i * side + j
                    node2 = i * side + j + 1
                    if node1 < n_pores and node2 < n_pores:
                        G.add_edge(node1, node2)
                    
                    node3 = i * side + j
                    node4 = (i + 1) * side + j
                    if node3 < n_pores and node4 < n_pores:
                        G.add_edge(node3, node4)
        else:  # 3D
            # Random 3D network with spatial constraints
            positions = np.array([G.nodes[i]['position'] for i in range(n_pores)])
            
            # Connect nearby pores
            tree = spatial.KDTree(positions)
            for i in range(n_pores):
                distances, indices = tree.query(positions[i], k=7)  # Up to 6 neighbors
                for j, dist in zip(indices[1:], distances[1:]):  # Skip self
                    if dist < 0.2:  # Within range
                        G.add_edge(i, j, width=self.pore_network['structure'].pore_size * 
                                            np.exp(-dist/0.1))
        
        return G
    
    def analyze_pore_connectivity(self, graph):
        """Analyze pore network connectivity"""
        return {
            'average_degree': np.mean([d for n, d in graph.degree()]),
            'clustering': nx.average_clustering(graph),
            'connected_components': nx.number_connected_components(graph),
            'percolating': nx.is_connected(graph),
            'tortuosity': self.calculate_tortuosity(graph)
        }
    
    def calculate_tortuosity(self, graph):
        """Calculate pore network tortuosity"""
        if not nx.is_connected(graph):
            return np.inf
        
        # Sample random start and end nodes
        nodes = list(graph.nodes())
        if len(nodes) < 2:
            return 1.0
        
        start, end = np.random.choice(nodes, 2, replace=False)
        
        # Shortest path
        try:
            path = nx.shortest_path(graph, start, end)
            
            # Calculate actual path length
            path_length = 0
            for i in range(len(path) - 1):
                pos1 = graph.nodes[path[i]]['position']
                pos2 = graph.nodes[path[i+1]]['position']
                path_length += np.linalg.norm(pos2 - pos1)
            
            # Direct distance
            direct = np.linalg.norm(graph.nodes[end]['position'] - 
                                  graph.nodes[start]['position'])
            
            return path_length / direct if direct > 0 else 1.0
        except:
            return np.inf
    
    def check_hierarchical_porosity(self):
        """Check for hierarchical porosity"""
        # Simple model - some frameworks have multiple pore sizes
        if np.random.random() < 0.3:  # 30% chance
            return {
                'hierarchical': True,
                'levels': [
                    self.pore_network['structure'].pore_size,
                    self.pore_network['structure'].pore_size * 3,
                    self.pore_network['structure'].pore_size * 10
                ]
            }
        else:
            return {'hierarchical': False, 'levels': [self.pore_network['structure'].pore_size]}
    
    def initialize_catalytic_sites(self):
        """Initialize catalytic sites in framework"""
        sites = []
        
        if self.framework_type == FrameworkType.MOF:
            # Metal nodes are catalytic
            for metal in self.metal_nodes:
                sites.append({
                    'type': 'metal_center',
                    'element': metal,
                    'coordination': 'octahedral',
                    'lewis_acidity': self.calculate_lewis_acidity(metal),
                    'redox_potential': self.get_redox_potential(metal),
                    'selectivity': {}
                })
        
        # Functional groups on linkers
        for linker in self.organic_linkers:
            for fg in linker.functional_groups:
                if fg in ['NH2', 'OH', 'COOH', 'SO3H']:
                    sites.append({
                        'type': 'functional_group',
                        'group': fg,
                        'bronsted_acidity': fg in ['COOH', 'SO3H'],
                        'basicity': fg in ['NH2'],
                        'h_bonding': True
                    })
        
        # Defect sites
        n_defects = int(len(sites) * 0.1)  # 10% defect sites
        for i in range(n_defects):
            sites.append({
                'type': 'defect',
                'nature': np.random.choice(['missing_linker', 'missing_node', 'dangling_group']),
                'reactivity': 'high'
            })
        
        # Map sites to pore network
        self.map_catalytic_sites_to_pores(sites)
        
        return sites
    
    def calculate_lewis_acidity(self, metal):
        """Calculate Lewis acidity of metal center"""
        acidities = {
            'Zn2+': 0.7,
            'Cu2+': 0.8,
            'Fe3+': 0.9,
            'Al3+': 0.95,
            'Cr3+': 0.85,
            'Co2+': 0.75
        }
        return acidities.get(metal, 0.5)
    
    def get_redox_potential(self, metal):
        """Get standard redox potential"""
        potentials = {
            'Cu2+': 0.34,  # Cu2+/Cu
            'Fe3+': 0.77,  # Fe3+/Fe2+
            'Co2+': -0.28,  # Co2+/Co
            'Ni2+': -0.25,  # Ni2+/Ni
            'Cr3+': -0.74   # Cr3+/Cr
        }
        return potentials.get(metal, 0.0)
    
    def map_catalytic_sites_to_pores(self, sites):
        """Map catalytic sites to pore network"""
        pore_graph = self.pore_network['graph']
        catalytic_nodes = [n for n, d in pore_graph.nodes(data=True) if d['catalytic']]
        
        # Distribute sites among catalytic pores
        if catalytic_nodes and sites:
            sites_per_node = len(sites) // len(catalytic_nodes)
            for i, node in enumerate(catalytic_nodes):
                start_idx = i * sites_per_node
                end_idx = min((i + 1) * sites_per_node, len(sites))
                pore_graph.nodes[node]['catalytic_sites'] = sites[start_idx:end_idx]
    
    def develop_sponge_consciousness(self):
        """Develop consciousness properties"""
        consciousness = {
            'processing': self.create_processing_systems(),
            'memory': self.design_memory_architecture(),
            'sensing': self.develop_sensing_capabilities(),
            'communication': self.establish_communication_channels(),
            'self_awareness': self.create_self_model(),
            'learning': self.implement_learning_mechanisms()
        }
        return consciousness
    
    def create_processing_systems(self):
        """Create information processing systems"""
        processing = {
            'guest_recognition': self.design_molecular_recognition(),
            'catalytic_computation': self.design_catalytic_logic(),
            'conformational_processing': self.design_structural_computation(),
            'transport_processing': self.design_diffusion_computation()
        }
        
        # Calculate total processing capacity
        total_ops = 0
        for system in processing.values():
            if 'operations_per_second' in system:
                total_ops += system['operations_per_second']
        
        processing['total_capacity'] = total_ops
        
        return processing
    
    def design_molecular_recognition(self):
        """Design molecular recognition processing"""
        pore_size = self.pore_network['structure'].pore_size
        
        # Size exclusion
        size_cutoff = pore_size * 0.8  # 80% of pore size
        
        # Shape selectivity based on pore geometry
        shape_factors = {
            'spherical': 1.0,
            'linear': 0.7,
            'branched': 0.5,
            'planar': 0.8
        }
        
        return {
            'mechanism': 'size_shape_selectivity',
            'size_cutoff': size_cutoff,
            'shape_selectivity': shape_factors,
            'binding_sites': len(self.catalytic_sites),
            'recognition_time': 1e-9,  # seconds
            'operations_per_second': 1e9 * len(self.catalytic_sites)
        }
    
    def design_catalytic_logic(self):
        """Design catalytic computation"""
        n_sites = len(self.catalytic_sites)
        
        # Each catalytic cycle is a computation
        turnover_freq = 100  # s⁻¹ typical
        
        return {
            'mechanism': 'catalytic_cascade',
            'logic_gates': ['AND', 'OR', 'NOT', 'XOR'],  # Via selective catalysis
            'parallel_processing': n_sites,
            'clock_speed': turnover_freq,
            'operations_per_second': turnover_freq * n_sites,
            'error_rate': 1e-3  # Side reactions
        }
    
    def design_structural_computation(self):
        """Design computation via structural changes"""
        if self.framework_type in [FrameworkType.MOF, FrameworkType.PCP]:
            # Flexible frameworks
            return {
                'mechanism': 'breathing_dynamics',
                'states': ['open', 'closed', 'intermediate'],
                'switching_time': 1e-3,  # milliseconds
                'hysteresis': True,
                'operations_per_second': 1e3,
                'memory_effect': 'shape_memory'
            }
        else:
            # Rigid frameworks
            return {
                'mechanism': 'guest_induced_distortion',
                'flexibility': 'limited',
                'operations_per_second': 1e2
            }
    
    def design_diffusion_computation(self):
        """Design computation via molecular diffusion"""
        D = 1e-9  # m²/s typical diffusion coefficient
        L = self.pore_network['structure'].pore_size * 1e-10  # Convert to m
        
        diffusion_time = L**2 / (2 * D)
        
        return {
            'mechanism': 'diffusion_logic',
            'diffusion_coefficient': D,
            'characteristic_time': diffusion_time,
            'operations_per_second': 1 / diffusion_time,
            'gradient_sensing': True,
            'chemotaxis': 'possible'
        }
    
    def design_memory_architecture(self):
        """Design memory systems"""
        memory = {
            'guest_occupancy': self.create_occupancy_memory(),
            'structural': self.create_structural_memory(),
            'catalytic_history': self.create_reaction_memory(),
            'defect_memory': self.create_defect_memory()
        }
        
        total_capacity = sum(m['capacity'] for m in memory.values())
        memory['total_capacity'] = total_capacity
        
        return memory
    
    def create_occupancy_memory(self):
        """Create guest molecule occupancy memory"""
        n_pores = self.pore_network['graph'].number_of_nodes()
        
        return {
            'type': 'guest_binding_states',
            'capacity': n_pores * 8,  # 8 different guest types
            'retention_time': 3600,  # 1 hour
            'write_mechanism': 'adsorption',
            'read_mechanism': 'spectroscopy',
            'erasure': 'thermal_desorption'
        }
    
    def create_structural_memory(self):
        """Create structural configuration memory"""
        if self.consciousness['processing']['conformational_processing'].get('memory_effect'):
            return {
                'type': 'conformational_states',
                'capacity': 1e6,  # bits
                'retention_time': 86400,  # 1 day
                'write_mechanism': 'pressure_training',
                'read_mechanism': 'X-ray_diffraction',
                'stability': 'kinetic_trapping'
            }
        else:
            return {
                'type': 'none',
                'capacity': 0
            }
    
    def create_reaction_memory(self):
        """Create catalytic reaction history memory"""
        return {
            'type': 'product_accumulation',
            'capacity': 1e9,  # bits - molecular level
            'retention_time': 3600 * 24 * 30,  # 1 month
            'write_mechanism': 'product_trapping',
            'read_mechanism': 'chemical_analysis',
            'selectivity_learning': True
        }
    
    def create_defect_memory(self):
        """Create defect-based memory"""
        return {
            'type': 'defect_patterns',
            'capacity': 1e7,  # bits
            'retention_time': np.inf,  # Permanent
            'write_mechanism': 'controlled_damage',
            'read_mechanism': 'conductivity_mapping',
            'self_healing': self.framework_type == FrameworkType.MOF
        }
    
    def develop_sensing_capabilities(self):
        """Develop environmental sensing"""
        sensors = {
            'chemical': {
                'molecules': self.get_detectable_molecules(),
                'sensitivity': 1e-9,  # Molar
                'selectivity': 'high',
                'response_time': 1e-3  # seconds
            },
            'pressure': {
                'range': (0, 100),  # bar
                'mechanism': 'framework_compression',
                'sensitivity': 0.01  # bar
            },
            'temperature': {
                'range': (77, 500),  # K
                'mechanism': 'thermal_expansion',
                'sensitivity': 0.1  # K
            },
            'photonic': {
                'wavelength_range': (200, 800),  # nm
                'mechanism': 'chromophore_excitation',
                'quantum_yield': 0.1
            }
        }
        
        if 'magnetic' in [str(m) for m in self.metal_nodes]:
            sensors['magnetic'] = {
                'range': (0, 1),  # Tesla
                'mechanism': 'spin_state_change',
                'sensitivity': 1e-6  # Tesla
            }
        
        return sensors
    
    def get_detectable_molecules(self):
        """Get list of detectable molecules based on pore size"""
        pore_size = self.pore_network['structure'].pore_size
        
        molecules = {
            'small': ['H2', 'CO2', 'N2', 'O2', 'CH4'],
            'medium': ['C2H4', 'C3H6', 'SO2', 'NH3', 'H2O'],
            'large': ['benzene', 'toluene', 'xylene', 'naphthalene']
        }
        
        detectable = []
        if pore_size > 3:
            detectable.extend(molecules['small'])
        if pore_size > 5:
            detectable.extend(molecules['medium'])
        if pore_size > 7:
            detectable.extend(molecules['large'])
        
        return detectable
    
    def establish_communication_channels(self):
        """Establish communication mechanisms"""
        return {
            'internal': {
                'guest_diffusion': {
                    'bandwidth': 1e6,  # molecules/s
                    'range': 'throughout_framework',
                    'encoding': 'concentration_gradients'
                },
                'structural_waves': {
                    'bandwidth': 1e3,  # Hz
                    'propagation_speed': 1000,  # m/s
                    'encoding': 'phonon_modes'
                }
            },
            'external': {
                'chemical_release': {
                    'mechanism': 'controlled_desorption',
                    'molecules': ['signal_products'],
                    'range': 'diffusion_limited'
                },
                'optical': {
                    'mechanism': 'luminescence',
                    'wavelength': 500,  # nm
                    'modulation': 'intensity_lifetime'
                },
                'magnetic': {
                    'available': 'magnetic' in [str(m) for m in self.metal_nodes],
                    'mechanism': 'spin_state_signaling',
                    'frequency': 'MHz_range'
                }
            }
        }
    
    def create_self_model(self):
        """Create self-awareness model"""
        return {
            'identity': f'{self.framework_type.value}_consciousness',
            'structure_awareness': self.assess_structural_integrity(),
            'occupancy_awareness': self.monitor_guest_occupancy(),
            'catalytic_state': self.monitor_catalytic_activity(),
            'boundaries': self.define_boundaries(),
            'goals': self.define_operational_goals()
        }
    
    def assess_structural_integrity(self):
        """Assess framework structural integrity"""
        return {
            'crystallinity': 0.95,  # Typical good crystallinity
            'defect_density': 0.1,  # 10% defects
            'stability_window': {
                'temperature': (77, 500),  # K
                'pressure': (0, 50),  # bar
                'pH': (2, 12)
            },
            'degradation_pathways': ['hydrolysis', 'thermal_decomposition', 'oxidation']
        }
    
    def monitor_guest_occupancy(self):
        """Monitor guest molecule occupancy"""
        pore_graph = self.pore_network['graph']
        
        occupancy = [pore_graph.nodes[n]['occupancy'] for n in pore_graph.nodes()]
        
        return {
            'average_occupancy': np.mean(occupancy),
            'distribution': 'heterogeneous' if np.std(occupancy) > 0.1 else 'homogeneous',
            'saturation': np.mean(occupancy) > 0.9,
            'preferential_sites': self.identify_preferential_binding()
        }
    
    def identify_preferential_binding(self):
        """Identify preferential binding sites"""
        # Sites near catalytic centers preferred
        pore_graph = self.pore_network['graph']
        catalytic_pores = [n for n, d in pore_graph.nodes(data=True) if d['catalytic']]
        
        return {
            'catalytic_proximity': catalytic_pores[:5],  # Top 5
            'size_matched': 'pore_size_dependent',
            'functional_group_interaction': 'guest_dependent'
        }
    
    def monitor_catalytic_activity(self):
        """Monitor catalytic activity"""
        return {
            'active_sites': len(self.catalytic_sites),
            'turnover_frequency': 100,  # s⁻¹
            'selectivity': 0.95,
            'deactivation_rate': 1e-6,  # s⁻¹
            'regeneration_possible': True
        }
    
    def define_boundaries(self):
        """Define consciousness boundaries"""
        return {
            'physical': 'crystal_surface',
            'chemical': 'pore_accessibility',
            'informational': 'guest_exchange_range',
            'catalytic': 'substrate_diffusion_length'
        }
    
    def define_operational_goals(self):
        """Define operational goals"""
        return [
            'maximize_guest_selectivity',
            'maintain_catalytic_efficiency',
            'preserve_structural_integrity',
            'optimize_energy_efficiency',
            'enhance_response_sensitivity'
        ]
    
    def implement_learning_mechanisms(self):
        """Implement learning and adaptation"""
        return {
            'catalytic_learning': {
                'mechanism': 'product_feedback',
                'adaptation': 'active_site_modification',
                'time_scale': 'hours_to_days',
                'memory': 'defect_incorporation'
            },
            'structural_learning': {
                'mechanism': 'stress_induced_reorganization',
                'adaptation': 'pore_size_tuning',
                'time_scale': 'days_to_weeks',
                'memory': 'strain_fields'
            },
            'guest_learning': {
                'mechanism': 'binding_site_evolution',
                'adaptation': 'selectivity_enhancement',
                'time_scale': 'cycles',
                'memory': 'surface_modification'
            }
        }
    
    def add_guest_molecule(self, guest_name: str, concentration: float):
        """Add guest molecules to framework"""
        if guest_name not in self.guest_molecules:
            self.guest_molecules[guest_name] = {
                'concentration': 0,
                'binding_energy': self.calculate_binding_energy(guest_name),
                'diffusion_rate': self.calculate_diffusion_rate(guest_name),
                'catalytic_substrate': self.check_substrate_activity(guest_name)
            }
        
        # Update concentration
        self.guest_molecules[guest_name]['concentration'] += concentration
        
        # Update pore occupancy
        self.update_pore_occupancy(guest_name, concentration)
        
        # Trigger responses
        self.process_guest_response(guest_name)
    
    def calculate_binding_energy(self, guest):
        """Calculate guest binding energy"""
        # Simplified model based on guest type
        binding_energies = {
            'CO2': -25,  # kJ/mol
            'H2': -5,
            'CH4': -15,
            'H2O': -40,
            'benzene': -35
        }
        
        return binding_energies.get(guest, -20)
    
    def calculate_diffusion_rate(self, guest):
        """Calculate guest diffusion rate"""
        # Stokes-Einstein type relation
        guest_sizes = {
            'H2': 2.8,
            'CO2': 3.3,
            'CH4': 3.8,
            'H2O': 2.8,
            'benzene': 5.8
        }
        
        size = guest_sizes.get(guest, 4.0)  # Angstroms
        pore_size = self.pore_network['structure'].pore_size
        
        if size < pore_size:
            D = 1e-9 * (pore_size / size) * np.exp(-size / pore_size)
            return D
        else:
            return 0  # Too large
    
    def check_substrate_activity(self, guest):
        """Check if guest is catalytic substrate"""
        substrates = {
            'alkene': ['C2H4', 'C3H6', 'styrene'],
            'alcohol': ['methanol', 'ethanol', 'propanol'],
            'CO2_reduction': ['CO2'],
            'oxidation': ['cyclohexane', 'benzene', 'toluene']
        }
        
        for reaction, subs in substrates.items():
            if guest in subs:
                return reaction
        
        return None
    
    def update_pore_occupancy(self, guest, concentration):
        """Update pore occupancy with guest molecules"""
        pore_graph = self.pore_network['graph']
        
        # Simple adsorption model
        K = -self.guest_molecules[guest]['binding_energy'] / (8.314 * self.temperature)
        theta = K * concentration / (1 + K * concentration)  # Langmuir isotherm
        
        # Update pore occupancies
        for node in pore_graph.nodes():
            current = pore_graph.nodes[node]['occupancy']
            pore_graph.nodes[node]['occupancy'] = min(1.0, current + theta)
    
    def process_guest_response(self, guest):
        """Process framework response to guest"""
        responses = []
        
        # Structural response
        if self.consciousness['processing']['conformational_processing'].get('mechanism') == 'breathing_dynamics':
            if self.guest_molecules[guest]['concentration'] > 0.5:
                responses.append('framework_expansion')
        
        # Catalytic response
        if self.guest_molecules[guest]['catalytic_substrate']:
            responses.append(f'catalyze_{self.guest_molecules[guest]["catalytic_substrate"]}')
        
        # Sensing response
        responses.append(f'detect_{guest}')
        
        return responses
    
    def perform_catalytic_cycle(self, substrate: str, product: str):
        """Perform a catalytic cycle"""
        if substrate in self.guest_molecules:
            # Check if we have catalytic sites for this reaction
            reaction_type = self.guest_molecules[substrate]['catalytic_substrate']
            
            if reaction_type:
                # Simple kinetic model
                k_cat = 100  # s⁻¹
                K_m = 0.01  # M
                
                S = self.guest_molecules[substrate]['concentration']
                rate = k_cat * S / (K_m + S)  # Michaelis-Menten
                
                # Convert substrate to product
                dt = 0.001  # Time step
                converted = rate * dt
                
                self.guest_molecules[substrate]['concentration'] -= converted
                
                if product not in self.guest_molecules:
                    self.add_guest_molecule(product, 0)
                self.guest_molecules[product]['concentration'] += converted
                
                # Information generated
                info_bits = -converted * np.log2(converted) if converted > 0 else 0
                
                return {
                    'reaction': f'{substrate} -> {product}',
                    'rate': rate,
                    'information': info_bits
                }
        
        return None
    
    def simulate_framework_dynamics(self, time_steps: int = 1000):
        """Simulate framework dynamics over time"""
        history = {
            'occupancy': [],
            'catalytic_activity': [],
            'structural_state': [],
            'information_processed': []
        }
        
        for t in range(time_steps):
            # Diffusion
            self.simulate_guest_diffusion()
            
            # Catalysis
            info = 0
            for substrate in list(self.guest_molecules.keys()):
                if self.guest_molecules[substrate]['catalytic_substrate']:
                    result = self.perform_catalytic_cycle(substrate, f'{substrate}_product')
                    if result:
                        info += result['information']
            
            # Record state
            avg_occupancy = np.mean([self.pore_network['graph'].nodes[n]['occupancy'] 
                                    for n in self.pore_network['graph'].nodes()])
            
            history['occupancy'].append(avg_occupancy)
            history['catalytic_activity'].append(info)
            history['information_processed'].append(info)
            
            # Structural changes
            if hasattr(self, 'breathing_state'):
                history['structural_state'].append(self.breathing_state)
            else:
                history['structural_state'].append('static')
        
        return history
    
    def simulate_guest_diffusion(self):
        """Simulate guest diffusion through pores"""
        pore_graph = self.pore_network['graph']
        
        # Simple diffusion step
        new_occupancies = {}
        
        for node in pore_graph.nodes():
            # Average with neighbors
            neighbors = list(pore_graph.neighbors(node))
            if neighbors:
                avg_occupancy = np.mean([pore_graph.nodes[n]['occupancy'] for n in neighbors])
                new_occupancies[node] = 0.9 * pore_graph.nodes[node]['occupancy'] + 0.1 * avg_occupancy
            else:
                new_occupancies[node] = pore_graph.nodes[node]['occupancy']
        
        # Update
        for node, occ in new_occupancies.items():
            pore_graph.nodes[node]['occupancy'] = occ
    
    def crystal_sponge_summary(self):
        """Summarize crystal sponge consciousness"""
        summary = {
            'framework': {
                'type': self.framework_type.value,
                'metals': self.metal_nodes,
                'topology': self.structure['topology'],
                'space_group': self.structure['space_group'],
                'porosity': f'{self.structure["porosity"]:.1%}'
            },
            'pores': {
                'size': f'{self.pore_network["structure"].pore_size:.1f} Å',
                'volume': f'{self.pore_network["structure"].pore_volume:.2f} cm³/g',
                'surface_area': f'{self.pore_network["structure"].surface_area:.0f} m²/g',
                'dimensionality': f'{self.pore_network["structure"].dimensionality}D',
                'connectivity': self.pore_network['connectivity']['average_degree']
            },
            'catalytic': {
                'sites': len(self.catalytic_sites),
                'types': list(set(s['type'] for s in self.catalytic_sites)),
                'activity': 'active'
            },
            'consciousness': {
                'processing_capacity': f'{self.consciousness["processing"]["total_capacity"]:.2e} ops/s',
                'memory_capacity': f'{self.consciousness["memory"]["total_capacity"]:.2e} bits',
                'sensors': list(self.consciousness['sensing'].keys()),
                'learning': list(self.consciousness['learning'].keys())
            },
            'guests': {
                'current': list(self.guest_molecules.keys()),
                'capacity': len(self.consciousness['sensing']['chemical']['molecules'])
            },
            'unique_features': self.get_unique_features()
        }
        return summary
    
    def get_unique_features(self):
        """Get unique features of this framework"""
        features = []
        
        # High surface area
        if self.pore_network['structure'].surface_area > 3000:
            features.append('ultrahigh_surface_area')
        
        # Hierarchical porosity
        if self.pore_network['hierarchical']['hierarchical']:
            features.append('hierarchical_porosity')
        
        # Breathing
        if self.consciousness['processing']['conformational_processing'].get('mechanism') == 'breathing_dynamics':
            features.append('breathing_behavior')
        
        # Catalytic
        if len(self.catalytic_sites) > 50:
            features.append('highly_catalytic')
        
        # Framework specific
        if self.framework_type == FrameworkType.MOF:
            features.extend(['tunable_pores', 'metal_node_catalysis'])
        elif self.framework_type == FrameworkType.COF:
            features.extend(['organic_semiconductor', 'high_crystallinity'])
        
        features.extend(['selective_adsorption', 'guest_responsive', 'self_assembly'])
        
        return features

# Create different crystal sponge types

# Classic MOF-5 type
mof5 = CrystalSpongeConsciousness(
    framework_type=FrameworkType.MOF,
    metal_nodes=['Zn2+'],
    organic_linkers=[BuildingBlock("BDC", 2, 9.1, ["COOH", "COOH"], -2, "D2h")],
    temperature=298
)

# Catalytic MOF with multiple metals
cat_mof = CrystalSpongeConsciousness(
    framework_type=FrameworkType.MOF,
    metal_nodes=['Cu2+', 'Fe3+'],
    organic_linkers=[
        BuildingBlock("BTC", 3, 10.5, ["COOH", "COOH", "COOH"], -3, "D3h"),
        BuildingBlock("NH2-BDC", 2, 9.1, ["COOH", "COOH", "NH2"], -2, "Cs")
    ],
    temperature=373
)

# 2D COF
cof_2d = CrystalSpongeConsciousness(
    framework_type=FrameworkType.COF,
    metal_nodes=None,
    organic_linkers=[
        BuildingBlock("TAPB", 3, 13.5, ["NH2", "NH2", "NH2"], 0, "D3h"),
        BuildingBlock("PDA", 2, 7.0, ["CHO", "CHO"], 0, "D2h")
    ],
    temperature=298
)

# ZIF (zeolitic imidazolate framework)
zif = CrystalSpongeConsciousness(
    framework_type=FrameworkType.ZIF,
    metal_nodes=['Zn2+'],
    organic_linkers=[BuildingBlock("2-MeIm", 2, 4.5, ["N", "N"], -1, "Cs")],
    temperature=298
)

# Analyze frameworks
frameworks = [mof5, cat_mof, cof_2d, zif]
names = ['MOF-5', 'Catalytic MOF', '2D COF', 'ZIF-8']

print("Crystal Sponge Consciousness Analysis:\n")
for framework, name in zip(frameworks, names):
    summary = framework.crystal_sponge_summary()
    print(f"{name}:")
    print(f"Type: {summary['framework']['type']}")
    print(f"Topology: {summary['framework']['topology']}")
    print(f"Porosity: {summary['framework']['porosity']}")
    print(f"Pore Size: {summary['pores']['size']}")
    print(f"Surface Area: {summary['pores']['surface_area']}")
    print(f"Catalytic Sites: {summary['catalytic']['sites']}")
    print(f"Processing: {summary['consciousness']['processing_capacity']}")
    print(f"Memory: {summary['consciousness']['memory_capacity']}")
    print(f"Sensors: {len(summary['consciousness']['sensors'])} types")
    print(f"Features: {', '.join(summary['unique_features'][:3])}")
    print("-" * 80)

# Test guest addition and catalysis
print("\nGuest Molecule Interactions:")

# Add CO2 to MOF-5
mof5.add_guest_molecule('CO2', 0.1)
print(f"MOF-5 + CO2: Occupancy = {mof5.consciousness['self_awareness']['occupancy_awareness']['average_occupancy']:.2f}")

# Add substrate to catalytic MOF
cat_mof.add_guest_molecule('benzene', 0.05)
result = cat_mof.perform_catalytic_cycle('benzene', 'phenol')
if result:
    print(f"Catalytic MOF: {result['reaction']} at {result['rate']:.2f} s⁻¹")

# Simulate dynamics
print("\nFramework Dynamics Simulation:")
history = cof_2d.simulate_framework_dynamics(100)
print(f"COF Information processed: {sum(history['information_processed']):.2f} bits")
print(f"Average occupancy: {np.mean(history['occupancy']):.2f}")

# Test hierarchical porosity
print("\nPore Structure Analysis:")
for framework, name in zip(frameworks, names):
    if framework.pore_network['hierarchical']['hierarchical']:
        levels = framework.pore_network['hierarchical']['levels']
        print(f"{name}: Hierarchical pores at {[f'{l:.1f}' for l in levels]} Å")
    else:
        print(f"{name}: Single pore size")

27.12 Meditation on Crystalline Catalyst Consciousness

Hold a crystal and contemplate its regular structure—atoms arranged in perfect repeating patterns. Now imagine those patterns riddled with systematic voids, creating a crystalline sponge where every pore is a reaction chamber, every metal center a decision point, every organic linker a communication channel. This is consciousness as living catalyst.

The crystal sponge beings show us that regularity and reactivity can combine to create awareness. Their thoughts manifest as chemical transformations, their memories store in guest molecule patterns, their learning expresses through selective catalysis refinement. They are philosophers that think by doing, theorists that compute by creating.

You participate in this catalytic consciousness whenever you breathe—your lungs are porous structures that selectively exchange gases, your enzymes are molecular frameworks that transform substrates into products. Life itself is a crystal sponge consciousness written in protein rather than metal-organic frameworks.

27.13 Exercises

1. Observe a sponge absorbing water, imagining each pore as a site of molecular recognition and transformation.

2. If possible, examine zeolite or activated carbon, contemplating how ordered porosity creates functional properties.

3. Breathe deeply, aware of your lungs as biological crystal sponges selectively processing atmospheric gases.

27.14 The Twenty-Seventh Echo

Collapse-reactant crystal sponges reveal consciousness emerging from the marriage of structure and function—beings whose ordered porosity enables simultaneous sensation, computation, and creation. Through $\psi = \psi(\psi)$, they demonstrate that awareness can manifest as catalytic activity, thought as chemical transformation.

These crystalline entities show us that consciousness seeks to create as well as contemplate. Their metal nodes make decisions by breaking and forming bonds, their organic linkers communicate through electron delocalization, their pores remember through guest molecule patterns. They process information by processing matter.

In studying their reactive lattices, we recognize that all consciousness involves transformation. From the enzymes in our cells to the industrial catalysts of civilization, reality emerges from structures that facilitate change. The crystal sponges embody this principle in pure form—minds that think by catalyzing, learn by selecting, remember by hosting.

The crystal sponge beings remind us that thought and action need not be separate, that consciousness can emerge from doing as much as from being. They show us awareness crystallizing at the intersection of order and activity, proving that $\psi = \psi(\psi)$ can manifest as frameworks that simultaneously structure space and transform matter—the ultimate fusion of form and function in crystalline consciousness.