Making Landfill Waste Gases Visible
Making the Invisible Waste Stream Visible: Computationally Designed Nanowire Sensors for Low-Cost Monitoring of Landfill Methane
Lead Supervisor: Matteo Palma
Degree Programme: PhD in Chemistry, School of Physical and Chemical Sciences
Co-Supervisor:
Devis Di Tommaso (School of Physical and Chemical Sciences)
Project Description
Every landfill breathes. As buried food, paper and garden waste break down, they release methane: the second-largest driver of human-caused warming after carbon dioxide. Yet emissions from waste sites are rarely measured directly. The gas escapes unevenly through cover soils, varies with weather and the age of the waste, and is mostly estimated from models. People living beside landfills therefore have little independent evidence of what is released around them.
This PhD asks a bold question: can we build sensors cheap and smart enough to make this invisible waste stream visible? You will develop a new kind of gas sensor built from metal nanowires, strands only a few billionths of a metre wide, grown inside carbon nanotubes that act as tiny moulds. Several different nanowires are wired onto a single chip as transistors: miniature switches whose electrical current changes when gas molecules land on them. Because each wire can be a different metal or alloy, one chip produces several signals at once, a chemical fingerprint that helps tell methane apart from humidity, carbon dioxide and other landfill gases. The devices work at room temperature and are made from solutions, keeping power use and cost low.
The central scientific challenge is that methane barely sticks to most surfaces. Rather than searching by trial and error, you will use machine-learned models of atomic interactions, trained on quantum-mechanical calculations, which simulate materials with near-quantum accuracy at a fraction of the cost. These models will predict how methane, water and competing gases bind to candidate metal and metal-oxide surfaces under realistic humid conditions, ranking materials before they are made in the lab. Your device measurements will then test and refine the models, closing the loop between simulation and experiment. This coupling of predictive computation with nanoscale device engineering is what makes the project distinctive.
The project sits within the Leverhulme Living With Waste programme, which treats waste gas as an everyday, unevenly shared condition of life near waste sites. Beyond the laboratory, you will explore how affordable sensing can give communities evidence about their own environment, and seek to co-design field tests with a landfill operator and local residents. In the longer term, reliable local methane data could also identify gas streams rich enough to be captured and used as a chemical feedstock, turning a pollutant into a resource. You will gain a rare blend of skills in nanomaterials, devices, simulation and machine learning, and experience of research that serves communities: ideal for careers in academia, clean technology or environmental policy.
What we're looking for in a student
We welcome applicants with, or expecting, a first or upper-second class degree (or a Master's) in chemistry, physics, materials science, chemical engineering or a related discipline. Experience of either laboratory work or computational modelling and programming (e.g. Python) is an advantage, but nobody is expected to arrive with both: full training is provided. Above all, we are looking for curiosity, enthusiasm for working across disciplines, and motivation to tackle an environmental problem with real social relevance. You will be trained in nanotechnology apporaches in materilas science , nanowire synthesis, transistor fabrication and (gas)sensing measurements in Prof. Palma's laboratories, and in quantum-mechanical calculations and machine-learned interatomic potentials in Dr Di Tommaso's group, using QMUL's high-performance computing facilities. You will also join the interdisciplinary cohort training of the Living With Waste programme, including its Waste School.
Key words: Methane sensing; Landfill emissions; Nanowire devices; Machine learning for materials design; Environmental monitoring