🔬 3D Science Lab — Interactive 3D STEM Education with 40+ Experiments Built Using Next.js and Three.js
<h2> Making Science Interactive </h2> <p>Traditional science education relies on static textbook diagrams and 2D illustrations. But science happens in three dimensions. I built <strong>3D Science Lab</strong> to make STEM education immersive — allowing students to interact with experiments in 3D, rotate models, zoom in on details, and truly understand the science behind what they see.</p> <h2> What is 3D Science Lab? </h2> <p>3D Science Lab is an interactive web platform featuring <strong>40+ 3D science experiments</strong> across four core disciplines:</p> <ul> <li> <strong>Physics</strong> — mechanics, optics, waves, electricity</li> <li> <strong>Chemistry</strong> — molecular structures, reactions, periodic table in 3D</li> <li> <strong>Biology</strong> — cell structures, organ systems,
Making Science Interactive
Traditional science education relies on static textbook diagrams and 2D illustrations. But science happens in three dimensions. I built 3D Science Lab to make STEM education immersive — allowing students to interact with experiments in 3D, rotate models, zoom in on details, and truly understand the science behind what they see.
What is 3D Science Lab?
3D Science Lab is an interactive web platform featuring 40+ 3D science experiments across four core disciplines:
-
Physics — mechanics, optics, waves, electricity
-
Chemistry — molecular structures, reactions, periodic table in 3D
-
Biology — cell structures, organ systems, DNA
-
Mathematics — geometric shapes, functions, calculus visualizations
Key Features
🧪 40+ Interactive Experiments
Each experiment is fully interactive — drag, rotate, zoom, and manipulate to explore scientific concepts hands-on.
🎮 Immersive 3D Rendering
Built with Three.js and React Three Fiber, the platform delivers smooth, WebGL-powered 3D graphics directly in the browser. No downloads, no plugins.
📱 Responsive Design
Works on desktop, tablet, and mobile. Science class shouldn't require a specific device.
⚡ Fast Performance
Optimized rendering pipeline ensures smooth 60fps interactions even with complex 3D models.
Tech Stack
-
Framework: Next.js 15
-
3D Engine: Three.js + React Three Fiber
-
Language: TypeScript
-
Animation: Framer Motion
-
UI Controls: Leva
-
Post-processing: React Three Postprocessing
Live Demo
🔗 3D Science Lab — Explore experiments now
Why It Matters
Studies show that interactive 3D learning improves retention by up to 80% compared to traditional 2D methods. 3D Science Lab brings this capability to every student with a browser — no expensive lab equipment needed.
Built by Rudra Sarker — Open Source Developer
Connect: X/Twitter | LinkedIn | GitHub
Sign in to highlight and annotate this article

Conversation starters
Daily AI Digest
Get the top 5 AI stories delivered to your inbox every morning.
More about
modelopen sourceplatform
Re-analysis of the Human Transcription Factor Atlas Recovers TF-Specific Signatures from Pooled Single-Cell Screens with Missing Controls
arXiv:2604.02511v1 Announce Type: new Abstract: Public pooled single-cell perturbation atlases are valuable resources for studying transcription factor (TF) function, but downstream re-analysis can be limited by incomplete deposited metadata and missing internal controls. Here we re-analyze the human TF Atlas dataset (GSE216481), a MORF-based pooled overexpression screen spanning 3,550 TF open reading frames and 254,519 cells, with a reproducible pipeline for quality control, MORF barcode demultiplexing, per-TF differential expression, and functional enrichment. From 77,018 cells in the pooled screen, we assign 60,997 (79.2\%) to 87 TF identities. Because the deposited barcode mapping lacks the GFP and mCherry negative controls present in the original library, we use embryoid body (EB) cel

VALOR: Value-Aware Revenue Uplift Modeling with Treatment-Gated Representation for B2B Sales
arXiv:2604.02472v1 Announce Type: new Abstract: B2B sales organizations must identify "persuadable" accounts within zero-inflated revenue distributions to optimize expensive human resource allocation. Standard uplift frameworks struggle with treatment signal collapse in high-dimensional spaces and a misalignment between regression calibration and the ranking of high-value "whales." We introduce VALOR (Value Aware Learning of Optimized (B2B) Revenue), a unified framework featuring a Treatment-Gated Sparse-Revenue Network that uses bilinear interaction to prevent causal signal collapse. The framework is optimized via a novel Cost-Sensitive Focal-ZILN objective that combines a focal mechanism for distributional robustness with a value-weighted ranking loss that scales penalties based on finan

On the Geometric Structure of Layer Updates in Deep Language Models
arXiv:2604.02459v1 Announce Type: new Abstract: We study the geometric structure of layer updates in deep language models. Rather than analyzing what information is encoded in intermediate representations, we ask how representations change from one layer to the next. We show that layerwise updates admit a decomposition into a dominant tokenwise component and a residual that is not captured by restricted tokenwise function classes. Across multiple architectures, including Transformers and state-space models, we find that the full layer update is almost perfectly aligned with the tokenwise component, while the residual exhibits substantially weaker alignment, larger angular deviation, and significantly lower projection onto the dominant tokenwise subspace. This indicates that the residual is
Knowledge Map
Connected Articles — Knowledge Graph
This article is connected to other articles through shared AI topics and tags.
More in Products

Rascene: High-Fidelity 3D Scene Imaging with mmWave Communication Signals
arXiv:2604.02603v1 Announce Type: new Abstract: Robust 3D environmental perception is critical for applications such as autonomous driving and robot navigation. However, optical sensors such as cameras and LiDAR often fail under adverse conditions, including smoke, fog, and non-ideal lighting. Although specialized radar systems can operate in these environments, their reliance on bespoke hardware and licensed spectrum limits scalability and cost-effectiveness. This paper introduces Rascene, an integrated sensing and communication (ISAC) framework that leverages ubiquitous mmWave OFDM communication signals for 3D scene imaging. To overcome the sparse and multipath-ambiguous nature of individual radio frames, Rascene performs multi-frame, spatially adaptive fusion with confidence-weighted fo

FusionBERT: Multi-View Image-3D Retrieval via Cross-Attention Visual Fusion and Normal-Aware 3D Encoder
arXiv:2604.02583v1 Announce Type: new Abstract: We propose FusionBERT, a novel multi-view visual fusion framework for image-3D multimodal retrieval. Existing image-3D representation learning methods predominantly focus on feature alignment of a single object image and its 3D model, limiting their applicability in realistic scenarios where an object is typically observed and captured from multiple viewpoints. Although multi-view observations naturally provide complementary geometric and appearance cues, existing multimodal large models rarely explore how to effectively fuse such multi-view visual information for better cross-modal retrieval. To address this limitation, we introduce a multi-view image-3D retrieval framework named FusionBERT, which innovatively utilizes a cross-attention-base

Unified and Efficient Approach for Multi-Vector Similarity Search
arXiv:2604.02815v1 Announce Type: new Abstract: Multi-Vector Similarity Search is essential for fine-grained semantic retrieval in many real-world applications, offering richer representations than traditional single-vector paradigms. Due to the lack of native multi-vector index, existing methods rely on a filter-and-refine framework built upon single-vector indexes. By treating token vectors within each multi-vector object in isolation and ignoring their correlations, these methods face an inherent dilemma: aggressive filtering sacrifices recall, while conservative filtering incurs prohibitive computational cost during refinement. To address this limitation, we propose MV-HNSW, the first native hierarchical graph index designed for multi-vector data. MV-HNSW introduces a novel edge-weight



Discussion
Sign in to join the discussion
No comments yet — be the first to share your thoughts!