Merge pull request #7 from tldr-group/rd/content-update-20260209

Rd/content update 20260209

Author: rmdocherty

README.md

@@ -55,6 +55,8 @@ yarn start
 ## TODO:
 
 - test merge rule
-- add Sheares!
-- add new publications: llama (rsc), evoxels, vulture, image rep (adv sci), hr-dv2 (adv int sys), BIL, prompt to protocol
-- add new projects: BIL, isb/isg, vulutre, evoxels
+
+- For Sheares:
+  - add Sheares!
+  - add new publications: prompt to protocol
+  - add new projects: isb/isg

public/content/images/projects/bil.png

@@ -1,15 +1,7 @@
 export const MainContent = () => {
   return (
     <div className="outlined-content">
-      <video
-        src="/assets/micro.webm"
-        playsInline
-        autoPlay
-        loop
-        muted
-        preload="auto"
-        style={{ maxWidth: "75%", objectFit: "scale-down" }}
-      />
+      <video src="/assets/micro.webm" playsInline autoPlay loop muted preload="auto" className="micro-gif" />
       <p>
         The Tools for Learning, Design and Research (tldr) group is a multidisciplinary team based in the Dyson school
         of Design engineering at Imperial College London. With activities spanning online education, generative design

public/content/images/projects/evoxels.png

@@ -7,7 +7,7 @@ const ProjectComponent = ({ projectData }: { projectData: Project }) => {
   return (
     <div className="outlined-content project-card">
       <div>
-        <img src={imagePath} alt={`${title}`} style={{ width: "200px", height: "200px", objectFit: "scale-down" }} />
+        <img src={imagePath} alt={`${title}`} style={{ width: "300px", height: "200px", objectFit: "scale-down" }} />
       </div>
       <div>
         <h3>{title}</h3>

public/content/images/projects/inpaint.gif

@@ -33,6 +33,20 @@
     "imagePath": "content/images/projects/imagerep.png",
     "order": 90
   },
+  {
+    "title": "Battery Imaging Library",
+    "desc": "The Battery Imaging Library (BIL) is one of the first open, curated collection of multi-modal and multi-length scale battery imaging datasets. There are 8 modalities, 80+ scans and over 500 billion voxels of open battery imaging data, from single particles up to full cells. ",
+    "outLinks": [
+      { "text": "GitHub", "link": "https://github.com/antonyvam/BatteryImagingLibrary" },
+      { "text": "Website", "link": "https://www.batteryimaginglibrary.com/" },
+      {
+        "text": "Preprint",
+        "link": "https://advanced.onlinelibrary.wiley.com/doi/10.1002/advs.202414149"
+      }
+    ],
+    "imagePath": "content/images/projects/bil.png",
+    "order": 89
+  },
   {
     "title": "SAMBA",
     "desc": "SAMBA (Segment Anything Model Based App) is a trainable segmentation web-app for materials science that uses Meta's Segment Anything Model for fast, high-quality labels and random forests for robust, generalizable segmentations.",
@@ -47,6 +61,32 @@
     "imagePath": "content/images/projects/samba_logo.png",
     "order": 80
   },
+  {
+    "title": "evoxels",
+    "desc": "evoxels is a differentiable physics framework for voxel-based microstructure simulations with a simple numpy interface and drop-in GPU accelerated backend (torch, JAX).",
+    "outLinks": [
+      { "text": "GitHub", "link": "https://github.com/daubners/evoxels" },
+      {
+        "text": "Preprint",
+        "link": "https://arxiv.org/abs/2507.21748"
+      }
+    ],
+    "imagePath": "content/images/projects/evoxels.png",
+    "order": 79
+  },
+  {
+    "title": "vulture",
+    "desc": "Efficient convolutional upsampling of DINOv2 features, which can then be used for trainable micrograph segmentation.",
+    "outLinks": [
+      { "text": "GitHub", "link": "https://github.com/tldr-group/vulture" },
+      {
+        "text": "Preprint",
+        "link": "https://arxiv.org/abs/2508.21529"
+      }
+    ],
+    "imagePath": "content/images/projects/vulture.png",
+    "order": 78
+  },
   {
     "title": "microlib",
     "desc": "microlib is a searchable collection of 87 3D microstructures of various materials, intended for use in materials research. These were generated from the DoITPoMS micrograph library using our inpainting and SliceGAN tools.",
@@ -104,7 +144,7 @@
         "link": "https://pubs.rsc.org/en/content/articlelanding/2023/dd/d2dd00120a"
       }
     ],
-    "imagePath": "content/images/projects/inpaint.gif",
+    "imagePath": "content/images/projects/inpaint.jpg",
     "order": 30
   },
   {