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. 2023 Feb 20;9(3):330–338. doi: 10.1021/acscentsci.3c00011

Community Resource for Innovation in Polymer Technology (CRIPT): A Scalable Polymer Material Data Structure

Dylan J Walsh †, Weizhong Zou †, Ludwig Schneider ‡, Reid Mello †, Michael E Deagen †, Joshua Mysona ‡, Tzyy-Shyang Lin †, Juan J de Pablo ‡, Klavs F Jensen †, Debra J Audus §, Bradley D Olsen †,*
PMCID: PMC10037456  PMID: 36968543

Short abstract

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The Community Resource for Innovation in Polymer Technology (CRIPT) data model is designed to address the high complexity in defining a polymer structure and the intricacies involved with characterizing material properties.

1. Introduction

Polymers have transformed the ways we heal, feed, clothe, shelter, and transport humanity,1−4 and their continual improvement remains a core scientific endeavor.5−9 Despite the advent of electronic publishing and electronic lab notebooks, the way we record, store, and share scientific data follows largely the same format as it has for many decades.10−14 The scientific community has recognized the need for improved data infrastructure which has led to the FAIR (findable, accessible, interoperable, and reusable) guiding principles to spur knowledge discovery and innovation by extending the longevity and repurposing of digital research assets.15,16 The challenge of representing and indexing polymers has hindered the development of data infrastructure, leading to small and disparate data sets or uncatalogued data.17−20 As a result, most polymer data are scattered across millions of articles and journals in multiple noninteroperable formats.21−29 The inaccessibility of metadata and data leads to massive inefficiencies and missed opportunities to solve many of our current and future problems with a simple search.30 Overall, the challenges mentioned above highlight the need for information solutions that make valuable research data discoverable and accessible.

Among the main barriers to developing information solutions for polymers is the fact that they are large stochastic molecules with hierarchical structures spanning multiple length scales from chemical bonds to large molecular assemblies (Figure 1).11,31,32 To date, there is no single representation that can fully define a polymeric material structure, thus making them hard to index.18 The stochastic nature of these chemical structures stems from the statistical chemical reactions that are used to produce them. This stochasticity brings about distributions in molecular mass, composition, and topology. A combination of structural descriptors (like a chemical drawing) and distribution information is therefore required to fully define the molecular structure. At larger length scales, microstructures develop from phenomena such as phase segregation and crystallization. These microstructures can be spatially ordered, disordered, or have local patches of order and disorder. The combination of multiple chemical descriptors and length scales of structure makes it extremely difficult to define these materials.

Figure 1.

Figure 1

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Depiction of the possible range of metadata and data for an example polymer: poly(styrene – block – ethylene oxide) copolymer. This includes synthesis of the polymer, processing (spin coating), microstructure, microstructure characterization (grazing incidence small-angle X-ray scattering), a computer friendly structural representation (BigSMILES), chemical characterization (molecular weight distributions and composition), and properties (differential scanning calorimetry data providing glass and melt transition temperatures, tensile test to measure Young’s modulus).

In practice, defining a polymeric structure and characterizing material properties are even more challenging as polymers have an extremely wide range of properties that often are not easily measured due to physical limitations (e.g., due to poor solubility).31,33 This leads to variable data availability, often providing only relative information or relying on theoretical models which require expert knowledge to put into context. In many cases, experimentally obtaining the desired information is impossible or intractable, and multiple partial correlative data points must be compiled to provide a surrogate value. Additionally, the processing history under which the material was made can strongly influence microstructure formation and properties. Thus, data sets that do not completely capture the relevant data and metadata will be ineffective at providing reliable information which severely hinders scientific efforts.

With the importance of polymers, there have been several initiatives to capture polymer materials data.18,22−26,28,29,34,35 Among the key technical innovations needed for success is a data model or blueprint for data organization. Among the simplest and most prevalent schemas for polymers has been the single table schema, where data are structured in a series of rows and columns.21,28,34 This type of schema is often implemented with polymer names as the key structural descriptor followed by a series of properties. In polymer science, name-based identification has limited capability in specifying a molecular structure, making attributing material properties to a structure ambiguous. These types of data sets tend to be small, focused on a limited set of common commercial polymers and properties. The next evolution in database schemas was the migration to single-document schemas.18,36−38 These databases store data in ‘documents’ sometimes called objects (i.e., scripted data interchanging formats, typically JavaScript Object Notation (JSON)) which encapsulate data and metadata relative to something of interest. For example, PolyDAT18 focuses its document on a single polymer of interest. These single document style schemas represent a significant improvement from table-based schemas as they introduce flexibility in what data can be stored. However, a major drawback is that the documents need to be organized around an object of interest (e.g., material or reaction of interest). Multidocument, graph, and relational styled databases have emerged as the next evolution in data schemas where data can be linked across documents, thus reducing the duplication of data and significantly improving the provenance of data.29,39,40 These data schemas allow for increased flexibility and robustness and contain many features that are essential for storing complex data at a large scale. Another key innovation has been the development of BigSMILES, which extends simplified molecular-input line-entry system (SMILES), a compact line chemical line notation for polymers.41 BigSMILES provides a human and computer interpretable structural representation that can be used as a key identifier for polymer data.

To address the need for a scalable polymer informatics solution, this work details the Community Resource for Innovation in Polymer Technology (CRIPT) data model. The goal of the CRIPT is to develop a community-driven data ecosystem for polymer science. At the core of CRIPT is a general graph data model that places an emphasis on capturing the metadata and data needed to accurately represent the complexities of polymer material. More specifically, CRIPT’s data structure is designed to capture everything from small-molecule and polymer synthesis, material processing, material and reaction properties, material characterization, raw experimental data, and both atomistic and coarse-grained simulations of systems with well-defined chemistries. The data model focuses on providing a highly granular and descriptive design with a strong aversion to ambiguity while seeking to be as comprehensive as possible. CRIPT is driven by FAIR15,16 and open-source principles42 to support its community driven mission. The following sections will initially cover the design philosophy and technical aspects that drove the construction of the CRIPT data model. This will be followed by a high-level overview of the main nodes that make up the CRIPT data model and how they are linked to form the graph structure. A few examples will be provided to demonstrate how the data model operates. Finally, aspects of the implementation of the data model are discussed. The Supporting Information contains a detailed discussion for each node and subobject along with additional examples.

2. Results And Discussion

2.1. Design Features/Philosophy

CRIPT’s data structure is a graph consisting of sets of vertices/nodes, that contain the stored data/metadata, and edges, which store the relationships between data. Every node has a series of attributes and subobjects. Subobjects provide a hierarchy for organization and grouping data within a node (see Supporting Information for details), and attributes are the individual pieces of information that are to be stored. The links between nodes (edges) are achieved with a globally unique and persistent identifier. The presence of the unique identifier of one node in another node signifies an edge between those two nodes in the graph.29,39,40

The flexibility of CRIPT stems from the arrangement of nodes in a wide range of configurations, and nodes themselves can be reconfigured. The high level of granularity of CRIPT can be seen in the storage of any type of process, property, and data with explicit specification of context. For example, chemical properties can be explicitly attributed to a component in a mixture, or even more granularly, the property can be associated with a fragment of a chemical structure with atom indexing within BigSMILES. CRIPT reduces ambiguity by providing and directing data entry through an ever-growing and curated controlled vocabulary. Data validation is an additional design layer that has been added to minimize ambiguity by ensuring uniformity in the entered data. The combination of these design features sets the stage for a robust and general material data model.

The features that make the CRIPT’s data model uniquely suited for polymers are the inclusion of process history, support for BigSMILES, and the integration of both experimental and computational data. Capturing process history is a critical detail as physical properties such as elastic modulus, toughness, transparency, etc. depend strongly on processing history. Thus the ‘same polymer’ can have a wide range of values for a property given its processing conditions. Moreover, the graph architecture supports the need to link characterization data across multiple synthesis and processing steps to enable the definition of complex polymer architectures. CRIPT directly supports BigSMILES as the main structural descriptor for polymers since it is human readable, machine-friendly, and has full support for the wide diversity of polymer structures.41 BigSMILES in the context of CRIPT can be viewed as defining an ensemble of possible structures for a stochastic polymer where the probability of observing each molecular state is specified with the additional properties captured by the data model (molecular mass, dispersity, etc.). The inclusion of BigSMILES provides an opportunity for polymer structural search as well. CRIPT’s data model takes the stance that both data from experiments and simulation should be supported at an equal level, improving cohesion of the research community and the breadth of accessible data.

The design of a data model for an entire community brings about several technical challenges regarding database robustness, performance, maintainability, and cost. CRIPT’s graph structure provides robustness by not storing the data relative to a reaction, material, or organizational approach. This enables a single user or multiple different users to enter data which will yield the same representation in the data model. One of the key performance considerations is maintaining fast searching as the size of the database grows, while the data model facilitates rapid graph traversal, indexing by node type, and other advanced database search solutions. Additionally, the reduction of duplicated data through referencing significantly lowers the amount of data that needs to be stored and searched, and persistent identifiers enable reusability. As with any data structure, the design will continue to evolve, and the modularity of the graph structure simplifies maintainability and extensibility as changes are isolated to each individual object, minimizing the cost of reworking/adding improvements (the growth of technical debt).

CRIPT desires to be comprehensive, but it is impractical to store the large and growing amount of data within CRIPT directly. To navigate this issue, CRIPT’s data model embraces federated data storage. Federated data storage is a more attractive alternative to one monolithic server, as the aggregation of all data into a single location is slow, and the database resources are consumed by moving large files around, making these systems much more resource intensive and costly.43 Support for federated data is realized in CRIPT’s data model by focusing on storing key values (such as property data, material identifiers, and processing information) and metadata relevant for discoverability (typically a uniform resource locator (URL)) within the data model while directing nonkey information (such as raw data files) to be stored elsewhere. This enables users to store their data on their preferred data services (like Amazon Web Services48 or university servers). The federated database architecture helps to support the community and a FAIR-driven mission by allowing for decentralization of data.15,16

2.2. Data Model

CRIPT’s data model has two levels of structuring: nodes, which are the primary objects that make up the CRIPT graph structure (nodes will be written in italics), and subobjects which are used to construct substructures within the nodes (see Supporting Information for details). The project, collection, experiment, and inventory nodes serve as an organizational tool within the data model and are represented with a grayscale. The material, process, computation, computational_process, and data nodes make the core data-rich nodes which are represented by bold colors. Additionally, there is a reference node for citing external sources of data and software for citing computational tools (both colored white). A short description and examples of the nodes are shown in Table 1; and detailed explanations can be found in the Supporting Information.

Table 1. List of CRIPT Nodes with a Short Description and Examplesa.

2.2.

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a

Abbreviations: Atom Transfer Radical Polymerization (ATRP), polystyrene-block-polybutadiene (PS-b-PB), polyethylene (PE), Nuclear Magnetic Resonance (NMR), Large-scale Atomic/Molecular Massively Parallel Simulator (LAMMPS).

The organizational nodes have a tree like structure (Figure 2) with project being the root node that represents a major scientific thrust or research group. The project node links to one or more collections in which a collection is roughly equal to a publication or the content of a final project report. A collection links to one or more experiments and/or inventories. An experiment in this context refers to the association of data which can be either a physical experiment in the lab or a simulation. The inventory node is a way for users to create a list of materials. Projects, collections, experiments, and inventories provide organizational tools to help the users who are entering data into the database.

Figure 2.

Figure 2

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A depiction of the high level graph structure of the CRIPT data model. The organizational nodes (gray nodes) have a tree graph structure and provide a system for indexing and organizing the complex data graph. The core nodes (colored nodes) have a directional graph structure mimicking the temporal relationship between the actual objects as data was generated.

The organizational graph is independent of the core graph structure, with only unidirectional referencing coming from the organization nodes to the core nodes (Figure 2). Structuring the data in this way makes the core data graph invariant to how users organize data in collection and experiment nodes. Specifically, a project node is linked to material nodes, and an experiment node is linked to process, data, computation, and computational_process nodes. Materials are only associated with a single project node, and those materials can only be used within that project to avoid issues of data integrity. For example, if a user referenced a material node from another project and then the material was deleted, this would create a broken reference leading to the loss of data integrity. Thus, the reuse of materials from other projects requires copying material nodes into the current project. Process, data, computation, and computational_process nodes all link to a single experiment. Overall, this allows users to organize the process, data, computation, or computational_process as they see fit without changing how the data is stored in the database. These design choices seek to minimize the unexpected loss of data integrity without creating a large user or infrastructure burden.

The core graph structure is highly variable depending on the experiment; however, all wet-lab experimental graphs start with defining a material node or set of material nodes. A material is a collection of the identifiers and properties of a chemical, mixture, or substance. For example, in a typical chemical synthesis (Figure 3), the set of materials that are first defined are the ingredients for a process node. The process node contains details about quantities, procedure, process conditions, equipment, etc. for a material transformation. A process node may represent chemical transformation (e.g., chemical reaction), physical transformations (e.g., extrusion), or sample preparation for characterization. An alternating pattern of material and process nodes serves as the backbone of the experimental graph with data or computation nodes attached to the materials and processes. Data nodes provide links to sample preparation and to raw or processed experimental data like from nuclear magnetic resonance (NMR), size-exclusion chromatography (SEC), or differential scanning calorimetry (DSC). Computation stores information related to the creation or transformation of data. In the example, the computation node stores the steps used to calculate material properties derived from the raw data. Multistep chemical processes, including nonlinear diverging and converging processes, are also naturally captured in the core graph structure (see examples ‘Diblock Bottlebrush Synthesis’ and ‘Chemical Reaction with Aliquots’ in Supporting Information).

Figure 3.

Figure 3

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Example of a core graph for poly(lactic acid) (PLA) filament. This example highlights a chemical synthesis (first process node) to produce PLA, followed by an extrusion process to produce PLA filament (second process node). Raw data are recorded at multiple stages of the experiment to characterize material and chemical properties, and computations are used to convert the scanning electron microscopy (SEM) images into material properties.

For computational experiments, a typical graph starts with a computation (Figure 4) to capture the initialization of the computational system and information regarding the set of procedures related to building the molecular structure, initialization of the simulation box, etc. The initial computation node will produce data nodes that store the configuration of a virtual material. In the example, the initialization computation node produces the configuration file for the unequilibrated state of a polymer. Data nodes are then passed into further computations to transform the virtual material between configurations (e.g., from nonequilibrated state to an equilibrated state). This pattern of alternating computations and data nodes is a core motif of the simulation graphs. From a virtual material configuration (data node), properties can be calculated (computation node) and attributed to a polymer (material node). The production of the material node places simulated material properties in the same position as experimentally determined material properties. The computational_process node is used to capture simulated reactions or physical transformations on virtual materials. The computational_process node requires both a material node and the corresponding configuration file (data node) as the ingredients of the process will produce a new postprocessing configuration file (data node). This new virtual material configuration will indirectly lead to a new material node as properties are extracted.

Figure 4.

Figure 4

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Example of a computational core graph for determining the toughness of a poly(dimethylsiloxane) (PDMS) network under uniaxial extension. The PDMS network is initialized (first computation node), equilibrated (second computation node), and properties are computed (middle computation nodes). The computed properties are stored in the ‘PDMS Network’ material node. The material is then taken through a uniaxial extension process (computational process) and toughness is computed.

2.3. Data Model Examples

To illustrate the data model, an example graph centered around the organizational nodes is provided (Figure 5). Every new research effort involves creating a project node, in this case ‘block copolymer library’. The first part of the project may involve collecting literature information about how to synthesize the targeted block copolymer. During this process, information about the monomers can be recorded in material nodes and collected into an inventory, ‘vinyl monomers’ for use in experiments. As the project progresses, kinetic experiments may be performed to determine the optimal reaction conditions to produce the targeted materials. This set of kinetic experiments is grouped into their own collection, ‘ATRP kinetics’. Following successful determination of the optimal reaction conditions, the targeted library block copolymers can be made and grouped into their own collection, ‘diblock synthesis’. This example highlights a simple graph for the organizational nodes as there are likely to be many more collections and experiments.

Figure 5.

Figure 5

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Example graph of the organizational nodes for the creation of a block copolymer library. The ‘block copolymer library’ project serves as the root of the organizational graph. Three collections are contained within the project: ‘ATRP literature research’, ‘ATRP kinetics experiments’, and ‘synthesis of the diblock library’. The ‘ATRP literature’ collection consists of an inventory of vinyl monomers. The ‘ATRP kinetics’ collection consists of 3 experiments that were performed at 3 different temperatures to determine optimal reaction conditions. The ‘Diblock Synthesis’ contains a single experiment which is the synthesis of the targeted poly(methyl methacrylate)-block-polystyrene (PMMA-b-PS) polymer.

For an example graph of a chemical synthesis, the anionic polymerization of styrene with sec-butyl lithium (secBuLi) in a mixture of tetrahydrofuran (THF) and toluene is illustrated in Figure 6.44 The chemical synthesis graph is grouped into a single experiment node. When defining a new experiment for a chemical process, it is recommended to start with the ingredient material nodes, followed by a process node, product material node, and finishing with the characterization data nodes. To define the first ingredient in this example, a material node for styrene is created by adding identifiers such as SMILES strings, and chemical names. Material properties, like density and molecular mass, etc. are also added to aid with calculating reagent amounts. Mixtures such as secBuLi in toluene can be represented by making the two pure material nodes (‘secBuLi’ and ‘Toluene’) followed by making a third material node which is a mixture of the two. With all the ingredients defined, the process node for the anionic polymerization is defined by specifying the quantity of each ingredient, experimental procedure, conditions (reaction time, temperature), and reaction properties (yield). In addition to experimental data, this example was inspired by a literature reference, which can be linked directly to the process node through a reference node. With the process defined, the product material node ‘Polystyrene’ can now be created by specifying the identity with BigSMILES and a chemical name. Property data such as number average molecular mass and dispersity can be added, and the raw data for both the 1H NMR and size exclusion chromatography (SEC) analysis can be attached through the creation of data nodes. In this case, the combination of a BigSMILES string and the molecular mass data from SEC provide a full definition of the polymer structure.

Figure 6.

Figure 6

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Graph for the chemical synthesis of polystyrene made by the secBuLi initiated anionic polymerization of styrene. The graph starts with ‘secBuLi’ and ‘Toluene’ combining to create a ‘secBuLi solution’ material node. The ‘secBuLi solution’, ‘Styrene’, ‘Toluene’, ‘THF’, and ‘Methanol’ are added to the ‘Anionic polymerization’ process which captures the chemical reaction. Polymerization kinetic data were collected for the process by 1H NMR. The polymerization procedure was inspired by a literature reference. The process yields a ‘Polystyrene’ material node which was characterized by SEC and 1H NMR.

A graph illustrating the investigation of the self-assembling behavior of a block copolymer in thin films (Figure 7) shows the applicability of the data model to polymer characterization. The block copolymer of interest was obtained from a vendor; thus, it was initially characterized with SEC and NMR prior to use. To prepare for the investigation, atomically smooth silicon wafers were cleaned with a plasma treatment and will serve as the substrate for the block copolymer assembly. The first processing approach was to dissolve the block copolymer in acetone and perform a blade coating process. This sample was characterized by AFM (atomic force microscopy) and GISAXS (grazing-incidence small-angle X-ray scattering) to determine the microstructure phase and domain spacing. This same sample was then thermally annealed and recharacterized with the same techniques. Following these studies, another film was produced from the original block copolymer sample by dissolving the sample in chlorobenzene and spin coating the solution onto the silicon wafer. The AFM and GISAXS were once again performed to characterize the microstructure.

Figure 7.

Figure 7

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Graph for the characterization of block copolymer films prepared by blade and spin coating. Starting on the left side of the figure, silicon wafers are cleaned by plasma treatment. The block copolymer was obtained from a vendor and characterized prior to the formation of films. Film 1 was generated by dissolving the block copolymer in acetone and blade coating the solution onto a cleaned silicon wafer. Film 2 was prepared by thermal annealing film 1. Film 3 was generated by dissolving the block copolymer in chlorobenzene and spin coating the solution onto a cleaned silicon wafer. All three films were characterized by AFM and GISAXS.

Computational characterization of bulk amorphous polyethylene via molecular dynamics simulations is illustrated in Figure 8.45 The simulations are conducted using the LAMMPS software with an input file generated by packing polymer chains in a simulation box. After an equilibration procedure with a series of steps to relax, quench, and anneal the system, the radius of gyration, persistence length, and thermal conductivity of the equilibrated polyethylene are measured. For the thermal conductivity measurement an additional heat transfer simulation was performed.

Figure 8.

Figure 8

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Graph for the thermal conductivity analysis of polyethylene using LAMMPS. The simulation graph starts by defining the packing of polyethylene coils in a simulation box within a computation node. After initialization, the equilibrated polymer configurations were obtained with a procedure to relax, quench, and anneal the simulated ensemble. The resulting equilibrated properties, including radius of gyration and persistence length, were computed. An additional heat transfer simulation was carried out to measure the thermal conductivity.

The following example depicts a graph for the extraction of polyolefin material properties from the literature for machine learning (Figure 9). The extracted data can be directly stored in the properties section of the material nodes, and then the material nodes can be organized into subdata sets with the use of inventories. The entire data set can be organized into a single collection. Citations back to the literature source can be made on a data point basis with the use of reference nodes.

Figure 9.

Figure 9

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Graph for the creation of a machine learning data set for the prediction of linear and branched polyolefin material properties. Material properties are stored in the material node and can be cited to the literature reference with a reference node. Inventory nodes can be used to group material nodes into subdata sets which can be linked to a single collection to group the entire data set together. Abbreviations: ultrahigh molecular weight polyethylene (UHMWPE), low density polyethylene (LDPE)

2.4. Implementation

To put the data model into practice, a Python46 software development kit (SDK) has been implemented and made available for download.49 The purpose of the Python SDK is to streamline the use of the data model, as manually writing data into the data model would be complex, time-consuming, and error-prone. To handle the referencing between nodes, globally unique and persistent identifiers are autogenerated for each node and used to provide bridges between nodes. In CRIPT the persistent identifiers are URLs as CRIPT is natively web-based (and representational state transfer (REST) compatible), although the use of URLs is not a requirement. Each node can be serialized for storage in any desired format (typically JSON) and transferred across various hardware, software, databases, and programming languages. The Python SDK also provides an opportunity to incorporate various data validation layers, such as type check, data ranges, unit dimensionality, and canonicalization to increase data uniformity. The Python SDK is written in such a way that additional validation methods can be smoothly added as the software evolves.

To extend the data model for implementation into a full software ecosystem, two additional nodes are included in the data model, user and group, whose purpose is to provide access control to data. A user node is created when an individual joins the CRIPT ecosystem and stores their user information. Among one of the key user attributes is ORCID (open researcher and contributor ID) ID which provides a unique and persistent digital identifier back to a specific person.47 This serves to ensure that all contributions to the database are appropriately attributed to the individual. Additionally, the ORCID ID can be used for login through the ORCID API (application programming interface). A group is an organization of multiple users, and a user can be part of multiple groups. The group node is where access control/ownership for all data lies, and the group node will point to all other nodes in the CRIPT data model. The decision to make groups the owner of data was motivated by users tending to change jobs, research groups, and organizations throughout their careers, and data are typically owned by the organization and not the individual. In the simplest case, group and project will have a one-to-one relationship; and the one-to-one relationship will only be broken when more granular access control is needed (see Supporting Information example ‘Across Control Within Projects’). The inclusion of these nodes serves to provide a key feature needed to link to user directories common in large organizations. For data that are desired to be shared with the whole community, the ‘public’ attribute can be set to true to enable the data’s inclusion in the public search.

3. Conclusion

This work defines a new graph data model that underpins the CRIPT digital ecosystem. The data model is designed to support the structuring of metadata and data for polymers from physical experiments, and both atomistic and coarse-grained simulations of systems with well-defined chemistries. The graph data structure provides flexibility as well as granularity in the data it represents. The connections in the graph provide an intuitive model for the typical material research workflow and allow for high-level information to be swiftly gleaned. Considerations were placed on designing the CRIPT data model to (1) scale for big data while maintaining efficient searching and (2) reduce duplicated data which significantly lowers the amount of data that needs to be stored. This graph-based approach to modeling material data provides the key advancements that the community needs to bring cheminformatics to polymers. Overall, having well-structured data will lead to new innovations and enable the rapid sharing of data and innovations across the scientific community.

Acknowledgments

This work was primarily funded by the National Science Foundation Convergence Accelerator award number 2134795.

Supporting Information Available

The Supporting Information is available free of charge at https://pubs.acs.org/doi/10.1021/acscentsci.3c00011.

  • A detailed discussion for each node, subobject, and additional examples (PDF)

  • Full data model representation for example found in Figure 6 (PDF)

The authors declare no competing financial interest.

Supplementary Material

oc3c00011_si_001.pdf (2MB, pdf)
oc3c00011_si_002.pdf (185.2KB, pdf)

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oc3c00011_si_001.pdf (2MB, pdf)
oc3c00011_si_002.pdf (185.2KB, pdf)

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