Pesquisadores do Instituto Karolinska, na Suanãcia, desenvolveram um pequeno sensor para detectar pesticidas em frutas em apenas alguns minutos. A técnica, descrita como uma prova de conceito em um artigo na revista Advanced Science,
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)
A deposição de nanopartaculas de chama foi usada para produzir nano-sensores robustos que podem detectar resíduos de pesticidas nassuperfÍcies da maçã em poucos minutos. Crédito: Haipeng Li e Georgios A Sotiriou.
Pesquisadores do Instituto Karolinska, na Suanãcia, desenvolveram um pequeno sensor para detectar pesticidas em frutas em apenas alguns minutos. A técnica, descrita como uma prova de conceito em um artigo na revista Advanced Science, usa nanopartaculas pulverizadas por chama feitas de prata para aumentar o sinal de produtos quamicos. Ainda em um esta¡gio inicial, os pesquisadores esperam que esses nano-sensores possam ajudar a descobrir pesticidas alimentares antes do consumo.
"Os relatórios mostram que atémetade de todas as frutas vendidas na UE contem resíduos de pesticidas que, em grandes quantidades, estãoligados a problemas de saúde humana", diz Georgios Sotiriou, pesquisador principal do Departamento de Microbiologia, Tumor e Biologia Celular do Instituto Karolinska e autor correspondente do estudo. "No entanto, as técnicas atuais de detecção de pesticidas em produtos aºnicos antes do consumo são restringidas na prática pelo alto custo e fabricação pesada de seus sensores. Para superar isso, desenvolvemos nano-sensores baratos e reprodutaveis que poderiam ser usados para monitorar vestagios de pesticidas frutaferos na loja, por exemplo."
Os novos nano-sensores empregam uma descoberta da década de 1970 conhecida como dispersão raman melhorada pelasuperfÍcie, ou SERS, uma poderosa técnica de sensoriamento que pode aumentar os sinais diagnósticos de biomoléculas emsuperfÍcies meta¡licas em mais de 1 milha£o de vezes. A tecnologia tem sido utilizada em diversas áreas de pesquisa, incluindo análise química e ambiental, bem como para detectar biomarcadores para diversas doena§as. No entanto, os altos custos de produção e a reprodutibilidade limitada em lote a lote tem dificultado atéagora a aplicação generalizada nos diagnósticos de segurança alimentar.
Tecnologia de pulverização de chamas
No presente estudo, os pesquisadores criaram um nano-sensor SERS usando spray de chama — uma técnica bem estabelecida e econa´mica para o depa³sito de revestimento meta¡lico — para fornecer pequenas gotaculas de nanopartaculas de prata em umasuperfÍcie de vidro.
"O spray de chama pode ser usado para produzir rapidamente filmes sers uniformes em grandes áreas, removendo uma das principais barreiras a escalabilidade", diz Haipeng Li, pesquisador de pa³s-doutorado no laboratório de Sotiriou e primeiro autor do estudo.
Os pesquisadores então ajustaram a distância entre as nanopartaculas de prata individuais para aumentar sua sensibilidade. Para testar sua capacidade de detecção de substâncias, eles aplicaram uma fina camada de corante rastreador em cima dos sensores e usaram um espectra´metro para descobrir suas impressaµes moleculares. Os sensores detectaram de forma confia¡vel e uniforme os sinais moleculares e seu desempenho permaneceu intacto quando testado novamente após 2,5 meses, o que ressalta seu potencial de vida útil e viabilidade para a produção em larga escala, de acordo com os pesquisadores.
Pesticidas detectados em maçãs
Para testar a aplicação prática dos sensores, os pesquisadores os calibraram para detectar baixas concentrações de parathion-ethyl, um inseticida agracola ta³xico que éproibido ou restrito na maioria dospaíses. Uma pequena quantidade de parathion-ethyl foi colocada em parte de uma maçã. Os resíduos foram posteriormente coletados com um cotonete que estava imerso em uma solução para dissolver as moléculas de pesticidas. A solução foi descartada no sensor, que confirmou a presença de pesticidas.
"Nossos sensores podem detectar resíduos de pesticidas nassuperfÍcies da maçã em um curto espaço de tempo de cinco minutos sem destruir a fruta", diz Haipeng Li. "Embora precisem ser validados em estudos maiores, oferecemos uma aplicação prática de prova de conceito para testes de segurança alimentar em escala antes do consumo."
Em seguida, os pesquisadores querem explorar se os nanosargundos podem ser aplicados a outras áreas, como a descoberta de biomarcadores para doenças especaficas no ponto de cuidado em configurações limitadas a recursos.