AD Aayam Dhakal

Cornell University · Jiang Laboratory

Engineering polymer networks for biological systems.

I’m Aayam Dhakal, an MS Chemical Engineering researcher at Cornell working at the interface of polymer science and biomaterials. I design zwitterionic hydrogel systems for extracellular vesicle preservation and stem-cell microenvironments.

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Current degreeMS Chemical Engineering
Research focusBiomaterials · Cell interactions
LocationIthaca, New York
Portrait of Aayam Dhakal
MS Chemical Engineering · Cornell University Jiang Laboratory · Zwitterionic biomaterials
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01 · Research

Research at the interface of polymer science, biology, and computational chemical engineering.

01

Extracellular vesicle preservation

Porous zwitterionic hydrogels for EV capture and room-temperature preservation.

I engineer porous zwitterionic matrices and study how pore architecture and transport influence EV loading, recovery, structural integrity, and biological functionality during storage and release.

Design variablesPorosity · transport · hydrogel chemistry
Biological systemExtracellular vesicles
EV STRUCTURE Extracellular vesicle Lipid membrane · surface proteins · internal cargo
Hover or focus an EV to inspect · move the cursor to perturb the matrix
02

Stem-cell microenvironments

Thermoresponsive zwitterionic hydrogels for CD34+ HSPC expansion.

I am developing PNIPAAm–poly(carboxybetaine)–PNIPAAm triblock copolymer hydrogels as ultralow-fouling 3D matrices, tuning polymer composition, physical crosslinking, stiffness, and viscoelasticity to support primitive HSPC phenotypes.

Polymer architecturePNIPAAm–PCB–PNIPAAm
Cell systemCD34+ hematopoietic stem & progenitor cells
Interactive transition

Drag the temperature slider to follow polymer association and formation of a physically crosslinked 3D gel.

20°C37°C
20°Cpolymer solution
solution3D gel
03

Computational chemical engineering

Machine learning for chemical processes, drug-property prediction, and environmental data.

Before moving into biomaterials, I worked across process systems and applied machine learning. Select a project to explore the technical workflow.

01
Feedforward neural network for benzene–toluene distillationOperating variables → separation behavior → FNN surrogate model
BENZENE–TOLUENE COLUMN PROCESS VARIABLES FEEDFORWARD NEURAL NETWORK
A chemical-process modeling workflow centered on benzene–toluene separation, using process variables and simulated separation behavior to train a feedforward neural-network representation of the system.
SystemBenzene–toluene distillationModelFeedforward neural networkPurposeProcess modeling & optimization
02
Aqueous solubility prediction for drug-like compounds and candidate APIsMolecular structure → descriptors → gradient-boosted models
DRUG-LIKE STRUCTURE MOLECULAR DESCRIPTORS LIGHTGBM / XGBOOST
A cheminformatics workflow for aqueous-solubility prediction, transforming chemical structure into machine-readable descriptors and using LightGBM and XGBoost for drug-development and pre-formulation screening.
PropertyAqueous solubilityContextDrug-like compounds / candidate APIsModelsLightGBM · XGBoost
03
Ground-sampling and satellite-assisted PM₂.₅ estimationParticulate sampling + remote sensing → machine-learning estimation
SATELLITE OBSERVATIONS GROUND SAMPLING ML MODEL PM₂.₅ SPATIAL ESTIMATE
An environmental data-integration workflow combining particulate-matter sampling, black-carbon related analysis, and satellite observations to estimate ground-level PM₂.₅ patterns.
TargetGround-level PM₂.₅InputsSampling · satellite observationsPurposeSpatial air-quality estimation

02 · Research journey

From computational chemical engineering to biomaterials.

03 · Industrial experience

Industrial experience across quality systems, production, and plant operations.

04 · Publications

Peer-reviewed work.

01
International Journal of Energy and Water Resources · 2025

Physio-chemical analysis of water from different altitudes of Kathmandu Valley, Nepal.

Mahara, P., Paudel, Y., Chaudhary, P., Gaihre, S., Dhakal, A., & Pandey, B.

DOI ↗

05 · Skills & tools

Skills in polymer synthesis, materials characterization, cell culture, and computation.

Polymer synthesis

Controlled chemistry to functional networks.

Polymerization and hydrogel fabrication workflows used to tune architecture, crosslinking, and soft-material behavior.

Characterization

Connect molecular structure to material behavior.

Analytical and mechanical techniques spanning polymer composition, molecular weight, morphology, particle analysis, and rheology.

Biological systems

Engineer materials around biological function.

Hands-on work with cell culture, extracellular vesicles, and biomaterial interfaces in 2D and 3D systems.

Computational

Use models to interrogate chemical systems.

Programming, machine learning, process simulation, and numerical workflows across chemical and environmental engineering problems.

Interactive technique view

ATRP

Controlled radical polymerization with reversible activation and deactivation.

Select any technique above to change the scientific sketch.

06 · Contact

Research conversations & collaborations.

I’m interested in connecting with researchers working in polymeric biomaterials, hydrogel engineering, extracellular vesicles, stem-cell microenvironments, and related areas.

ad2437@cornell.edu ↗LinkedIn ↗